{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c4ab49e-574e-4123-a675-6b2f6ce0bb3d",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    " # Using Quantum Computers to Boost Whitebox Fuzzing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11339512-eb70-4c84-b661-7bc411b00e8e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Introduction\n",
    "\n",
    "In this demonstration we will show how to harness the power of quantum computers for **enhancing software security**. Specifically, We will use the Quantum Grover's algorithm for boosting the process of whitebox fuzzing.\n",
    "\n",
    "According to [[1](#Whitebox)], the \"killer-app\" for whitebox fuzzing is the **testing of file\\packet parsers**. Any vulnerability of such parser might result with a very costy security patch, so one would want to invest much effort in validating that such code is protected.\n",
    "\n",
    "### Fuzzing\n",
    "\n",
    "<center><img src=\"https://docs.classiq.io/resources/fuzzing.jpeg\" width=700/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5bfb13-4b5b-4758-a976-ab4b2449f5ea",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "Fuzzing is a dynamic code testing technique that provides random data, known as \"fuzz,\" to the inputs of a program. The goal is to find bugs, security loopholes, or other unexpected behavior in the software. By feeding the program various input combinations, a fuzzer aims to uncover weaknesses that might be exploited maliciously."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a8c0da-0d43-4836-8d62-719e28bc9e2c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "### Whitebox Fuzzing\n",
    "\n",
    "Whitebox fuzzing, in particular, involves having access to the internal structure and code of the program. It combines static and dynamic analysis to not only execute the program with random inputs but also to achieve maximum code coverage, ensuring that all possible execution paths have been tested. This allows for more targeted and efficient testing. It usually consists of a \"symbolic execution\" part, in which the program is emulated in order to explore various branches, and gathered into a set of contraints.\n",
    "\n",
    "The constraints are solved by a constraint solver in order to generate new fuzzing input to the program.\n",
    "\n",
    "### Toy Example\n",
    "Let us emphasize the importance of whitebox testing in the following toy example. Take the following function, for which we want to trigger all relevant code flows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b140d6c-6d90-4762-b90f-092df5e5643c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:31.894137Z",
     "iopub.status.busy": "2024-05-07T15:23:31.893944Z",
     "iopub.status.idle": "2024-05-07T15:23:31.903343Z",
     "shell.execute_reply": "2024-05-07T15:23:31.902793Z"
    },
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def foo(x: int, y: int):\n",
    "    if x == 12:\n",
    "        if y > 3:\n",
    "            return \"a\"\n",
    "        return \"b\"\n",
    "    if y + x < 9:\n",
    "        if (x * y) % 4 == 1:\n",
    "            return \"c\"\n",
    "        return \"d\"\n",
    "    return \"e\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cef65e3-644f-4615-824d-7ff52c5fb7c3",
   "metadata": {},
   "source": [
    "Now (due to simulation limitations) say that x, y are 6 bit integers, so they are in the range [0, 63].\n",
    "Let's try to get all the outputs of `foo` in a black-box way, e.g. by sampling random inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "61c074ef-f6e6-4d1b-86c4-1b87ea14f598",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:31.906335Z",
     "iopub.status.busy": "2024-05-07T15:23:31.905963Z",
     "iopub.status.idle": "2024-05-07T15:23:32.459377Z",
     "shell.execute_reply": "2024-05-07T15:23:32.458522Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "np.random.seed(3)\n",
    "x_samples = np.random.randint(0, 63, 500)\n",
    "y_samples = np.random.randint(0, 63, 500)\n",
    "\n",
    "outputs = [foo(x, y) for x, y in zip(x_samples, y_samples)]\n",
    "\n",
    "\n",
    "def plot_outputs(outputs):\n",
    "    char_counts = Counter(outputs)\n",
    "\n",
    "    # Data for plotting\n",
    "    chars = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n",
    "    counts = list(char_counts.values())\n",
    "\n",
    "    # Plotting\n",
    "    plt.bar(char_counts.keys(), char_counts.values())\n",
    "    plt.xlabel(\"Output\")\n",
    "    plt.ylabel(\"Occurrences\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_outputs(outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d89ee0c-cdbf-4e0d-a2f8-5d402c5158e5",
   "metadata": {},
   "source": [
    "We Can see that with 500 inputs, we only reached 3 out of the 5 different outputs for `foo`.\n",
    "\n",
    "However, by following the flow of `foo`, we can generate the following constraints to the function:\n",
    "- \"a\": $ (x = 12) \\land (y \\gt 3) $\n",
    "- \"b\": $ (x = 12) \\land (y \\leq 3) $\n",
    "- \"c\": $ (x + y \\lt 9) \\land (x \\neq 12) \\land (x \\times y \\mod 4 = 1)$\n",
    "- \"d\": $ (x + y \\lt 9) \\land (x \\neq 12) \\land (x \\times y \\mod 4 \\neq 1)$\n",
    "- \"e\": $ (x \\neq 12) \\land (x + y \\lt 9)$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f88fe21-85b5-4bab-838a-ce781dc91f1f",
   "metadata": {},
   "source": [
    "Now, In order to trigger each of the different outputs, we need to find inputs that satisfy the constraints. Some of the constraints might not be satisfiable for any input. Although this toy example is easy, the general case, which is an instance of Constraints Satisfaction Problem (CSP), is computationaly hard and belongs to the $\\text{NP-Complete}$ complexity class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c343ff-f666-47bf-bb52-13fe2a428bc3",
   "metadata": {},
   "source": [
    "## Here Comes the Quantum Part!\n",
    "\n",
    "### Grover's Algorithm\n",
    "The physical nature of quantum computer can be harnessed to generate inputs to the function. Specifically, we can create a 'superposition' of all different inputs - a physical state which holds all the possible assignments to the function inputs, for which we compute whether the constraints is fulfilled. The quantum computer allows only a single classical output of the variable. That where Grover's algorithm [[1](#Gro97),[2](#GroWiki)] takes place - it can generate \"good\" samples with a high probability, achieving a quadratic(!) speedup over a classical brute-force approach.\n",
    "\n",
    "### Oracle Function\n",
    "In the heart of the algorithm, one needs to implement an oracle that compute for each state:\n",
    "\n",
    "$\n",
    "O |x\\rangle =\n",
    "\\begin{cases}\n",
    "-|x\\rangle & \\text{if } f(x) = 1 \\\\\n",
    "|x\\rangle & \\text{otherwise}\n",
    "\\end{cases}\n",
    "$\n",
    "\n",
    "Classiq has a built-in Arithmetic engine for computing such oracles. Specifically, lets take the hardest constraint:\n",
    "\n",
    "* **\"c\": $ (x + y < 9) \\land (x \\neq 12) \\land (x \\times y \\mod 4 = 1)$**\n",
    "\n",
    "We eliminate the $x \\neq 12$ as it is already satisfied given the first clause, and create a predicate function for it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "811c8874-e4de-4226-bc8c-d09fd6dc5536",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:32.464861Z",
     "iopub.status.busy": "2024-05-07T15:23:32.463507Z",
     "iopub.status.idle": "2024-05-07T15:23:35.148933Z",
     "shell.execute_reply": "2024-05-07T15:23:35.148285Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import QBit, QNum, qfunc\n",
    "from classiq.qmod.symbolic import logical_and\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def my_predicate(x: QNum, y: QNum, res: QBit) -> None:\n",
    "    res ^= logical_and((x + y < 9), ((x * y) % 4 == 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0495ab1-cfde-4bd3-9488-3cc7417cfef2",
   "metadata": {},
   "source": [
    "And we create a phase oracle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cbec1b8-15e3-4a19-9916-0bf852f344af",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:35.152208Z",
     "iopub.status.busy": "2024-05-07T15:23:35.151518Z",
     "iopub.status.idle": "2024-05-07T15:23:35.157302Z",
     "shell.execute_reply": "2024-05-07T15:23:35.156582Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import H, Output, QCallable, X, allocate, within_apply\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def prep_minus(out: Output[QBit]) -> None:\n",
    "    allocate(1, out)\n",
    "    X(out)\n",
    "    H(out)\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def my_oracle(predicate: QCallable[QBit]) -> None:\n",
    "    aux = QBit(\"aux\")\n",
    "    within_apply(compute=lambda: prep_minus(aux), action=lambda: predicate(aux))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e24742e5-11e5-4298-a134-db9699030a51",
   "metadata": {},
   "source": [
    "Let's see how a quantum oracle looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63ac21a7-0e24-452d-914f-cd57c06c63a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:35.159870Z",
     "iopub.status.busy": "2024-05-07T15:23:35.159369Z",
     "iopub.status.idle": "2024-05-07T15:23:42.715138Z",
     "shell.execute_reply": "2024-05-07T15:23:42.714375Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/52e3cff0-1eba-43c0-ad58-8fb6477717fd?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import (\n",
    "    Constraints,\n",
    "    allocate_num,\n",
    "    create_model,\n",
    "    set_constraints,\n",
    "    show,\n",
    "    synthesize,\n",
    ")\n",
    "\n",
    "REGISTER_SIZE = 6\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def main():\n",
    "    x = QNum(\"x\")\n",
    "    y = QNum(\"y\")\n",
    "    allocate_num(REGISTER_SIZE, False, 0, x)\n",
    "    allocate_num(REGISTER_SIZE, False, 0, y)\n",
    "    my_oracle(predicate=lambda q: my_predicate(x, y, q))\n",
    "\n",
    "\n",
    "qmod_oracle = create_model(main)\n",
    "qmod_oracle = set_constraints(qmod_oracle, Constraints(max_width=25))\n",
    "qprog_oracle = synthesize(qmod_oracle)\n",
    "show(qprog_oracle)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa1a052-25f5-4e7e-88cb-a0694bfac787",
   "metadata": {},
   "source": [
    "<center><img src=\"https://docs.classiq.io/resources/oracle.jpg\" width=700/></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ade9bf0-b6da-4407-8840-02678481d3e2",
   "metadata": {},
   "source": [
    "### Full Grover's Circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7cd7593-a777-46a1-8a62-bdec7c1fae27",
   "metadata": {},
   "source": [
    "Now create the full circuit implementation the Grover's algorithm:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08919134-2f3f-4dc4-9a00-74306e4379c2",
   "metadata": {},
   "source": [
    "#### State Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a11a3fa1-5131-47d8-b03e-cbe31d4c7309",
   "metadata": {},
   "source": [
    "Load the uniform superposition state - over all possible input assignments to `foo`, by using the `hadamard_transform`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a97c44d-863c-4167-8ce5-1fe96b1fb414",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:42.718128Z",
     "iopub.status.busy": "2024-05-07T15:23:42.717721Z",
     "iopub.status.idle": "2024-05-07T15:23:42.721255Z",
     "shell.execute_reply": "2024-05-07T15:23:42.720533Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import hadamard_transform\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def my_sp(x: QNum, y: QNum):\n",
    "    hadamard_transform(x)\n",
    "    hadamard_transform(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d80a2d-7de8-416e-b006-cf4349a6ab94",
   "metadata": {},
   "source": [
    "#### Grover Operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e7ccbe7c-1ee8-4e69-bce9-bbe86e154646",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:42.724271Z",
     "iopub.status.busy": "2024-05-07T15:23:42.723700Z",
     "iopub.status.idle": "2024-05-07T15:23:42.729246Z",
     "shell.execute_reply": "2024-05-07T15:23:42.728623Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import X, bind, control, invert\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def zero_predicate(x: QNum, y: QNum, res: QBit):\n",
    "    joined = QNum(\"joined\", x.size + y.size, False, 0)\n",
    "    bind([x, y], joined)\n",
    "    control(joined == 0, lambda: X(res))\n",
    "    bind(joined, [x, y])\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def my_diffuser(sp_operand: QCallable[QNum, QNum], x: QNum, y: QNum):\n",
    "\n",
    "    within_apply(\n",
    "        lambda: invert(lambda: sp_operand(x, y)),\n",
    "        lambda: my_oracle(predicate=lambda q: zero_predicate(x, y, q)),\n",
    "    )\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def my_grover_operator(oracle_operand: QCallable, diffuser_operand: QCallable):\n",
    "    oracle_operand()\n",
    "    diffuser_operand()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc30ecc6-9fb8-4a38-ad32-2b94b268defb",
   "metadata": {},
   "source": [
    "#### Grover Repetitions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b4d267-e8b4-4b60-bc40-ee52bc4d0443",
   "metadata": {},
   "source": [
    "The algorithm includes applying a quantum oracle in repetition, such that the probability to sample a good state \"rotates\" from low to high. Without knowing the concentration of solution before hand (which is the common case), one might overshoot with too many repetitions and not get a solution. Fixed Point Amplitude Amplification (FFPA) ([4](#FFPA)) for example, is a modification to the basic Grover algorithm, which does not suffer from the overshoot issue. However, here for simplicity we will use the basic Grover's Algorithm. We assume that this specific state only satisfied for a specific input, and calculate the number of oracle repetitions required:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "923881de-797c-4771-b673-2bf31d26b99c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:42.731607Z",
     "iopub.status.busy": "2024-05-07T15:23:42.731422Z",
     "iopub.status.idle": "2024-05-07T15:23:42.735307Z",
     "shell.execute_reply": "2024-05-07T15:23:42.734616Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.0\n"
     ]
    }
   ],
   "source": [
    "GROVER_REPEATS = np.pi / 4 * np.sqrt(2 ** (2 * REGISTER_SIZE) / 2)\n",
    "GROVER_REPEATS = np.round(GROVER_REPEATS)\n",
    "print(GROVER_REPEATS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6572e941-0b83-46d2-826a-fa4ab9406503",
   "metadata": {},
   "source": [
    "This is indeed ~ the square root of the number of possible assignment - $2^{12}$ in this case!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e513d93-948a-4390-a316-2a417879fb18",
   "metadata": {},
   "source": [
    "In order to save simulation time, will simplify even further, and use only several grover repetitions, to show that this raises the probability to sample a \"c\" input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b20cee4c-aec8-44c0-805b-e30e7f680bc7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:42.737911Z",
     "iopub.status.busy": "2024-05-07T15:23:42.737367Z",
     "iopub.status.idle": "2024-05-07T15:23:42.770131Z",
     "shell.execute_reply": "2024-05-07T15:23:42.769483Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import power\n",
    "\n",
    "\n",
    "@qfunc\n",
    "def main(x: Output[QNum], y: Output[QNum]):\n",
    "\n",
    "    allocate_num(REGISTER_SIZE, False, 0, x)\n",
    "    allocate_num(REGISTER_SIZE, False, 0, y)\n",
    "    my_sp(x, y)\n",
    "    power(\n",
    "        4,\n",
    "        lambda: my_grover_operator(\n",
    "            lambda: my_oracle(predicate=lambda q: my_predicate(x, y, q)),\n",
    "            lambda: my_diffuser(lambda x, y: my_sp(x, y), x, y),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "\n",
    "qmod_grover = create_model(main)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b224aa49-4a3b-4ed3-ab59-6f47ca9840b6",
   "metadata": {},
   "source": [
    "We will also set constraints for the resulting circuit, so we can simulate it on a quantum simulator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89b49e01-a70d-44ac-9996-ccea5f35194b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:42.773000Z",
     "iopub.status.busy": "2024-05-07T15:23:42.772527Z",
     "iopub.status.idle": "2024-05-07T15:23:42.792403Z",
     "shell.execute_reply": "2024-05-07T15:23:42.791735Z"
    }
   },
   "outputs": [],
   "source": [
    "qmod_grover = set_constraints(qmod_grover, Constraints(max_width=25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ecc38f0-0931-4e0f-a804-ac0eb4c12d08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:42.794760Z",
     "iopub.status.busy": "2024-05-07T15:23:42.794570Z",
     "iopub.status.idle": "2024-05-07T15:23:42.811025Z",
     "shell.execute_reply": "2024-05-07T15:23:42.810402Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod_grover, \"whitebox_fuzzing\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b05f3dbe-8578-4594-a5dc-ef50480c1b62",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Synthesizing the Model\n",
    "\n",
    "We proceed by synthesizing the circuit using Classiq's synthesis engine. The synthesis takes the should takes the high-level model definition and creates a quantum circuits implementation. It should take approximately several seconds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6bc751f4-9650-4d55-bd1f-b7694651cc4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:23:42.813560Z",
     "iopub.status.busy": "2024-05-07T15:23:42.813173Z",
     "iopub.status.idle": "2024-05-07T15:24:24.549813Z",
     "shell.execute_reply": "2024-05-07T15:24:24.549170Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qprog_grover = synthesize(qmod_grover)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a7d37f8-4829-4d1b-8f11-bb1313be6bf9",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    },
    "tags": []
   },
   "source": [
    "### Showing the Resulting Circuit\n",
    "\n",
    "After Classiq's synthesis engine has finished the job, we can show the resulting circuit in the interactive GUI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf3739f7-cc7a-4db9-8b3b-fb33d4296aff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:24:24.554510Z",
     "iopub.status.busy": "2024-05-07T15:24:24.554012Z",
     "iopub.status.idle": "2024-05-07T15:24:24.760458Z",
     "shell.execute_reply": "2024-05-07T15:24:24.759774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/87e46e1b-0a79-441f-916e-a37c3bdf84e7?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "show(qprog_grover)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6b5b0da-718d-4ed9-ab5c-6f3ad94c1022",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Executing the circuit\n",
    "\n",
    "Lastly, we can execute the resulting circuit with Classiq's execute interface, using the `execute` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f98bda8f-c128-4f3f-a21c-f12a1964d24c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:24:24.763192Z",
     "iopub.status.busy": "2024-05-07T15:24:24.762660Z",
     "iopub.status.idle": "2024-05-07T15:33:07.555327Z",
     "shell.execute_reply": "2024-05-07T15:33:07.554711Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute, set_quantum_program_execution_preferences\n",
    "from classiq.execution import (\n",
    "    ClassiqBackendPreferences,\n",
    "    ClassiqSimulatorBackendNames,\n",
    "    ExecutionPreferences,\n",
    ")\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(\n",
    "        backend_name=ClassiqSimulatorBackendNames.SIMULATOR\n",
    "    ),\n",
    "    num_shots=500,\n",
    ")\n",
    "\n",
    "qprog_grover = set_quantum_program_execution_preferences(\n",
    "    qprog_grover, backend_preferences\n",
    ")\n",
    "raw_results = execute(qprog_grover).result()\n",
    "results = raw_results[0].value"
   ]
  },
  {
   "attachments": {
    "7c3b6d49-4e52-4fbc-8129-b51f46fdf098.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAACI4AAANACAYAAABHNLQUAAAMbWlDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnluSkJCEEkBASuhNEKkBpITQAkgvgo2QBBJKjAlBxV4WFVy7iIANXRVRbCsgduzKotj7YkFFWRd1saHyJiSg677yvfN9c++fM2f+U+5M7j0AaH3gSaV5qDYA+ZICWUJ4MHN0WjqT9AwgAAN0YAtoPL5cyo6LiwZQBu5/l3c3oDWUq85Krn/O/1fRFQjlfACQsRBnCuT8fIiPA4BX8aWyAgCISr3V5AKpEs+GWE8GA4R4lRJnq/B2Jc5U4cP9NkkJHIgvA6BB5fFk2QDQ70E9s5CfDXnonyF2lQjEEgC0hkEcwBfxBBArYx+Wnz9Ricshtof2UohhPICV+R1n9t/4Mwf5ebzsQazKq180QsRyaR5v6v9Zmv8t+XmKAR+2cFBFsogEZf6whrdyJ0YpMRXiLklmTKyy1hB/EAtUdQcApYgUEckqe9SEL+fA+gEDiF0FvJAoiE0gDpPkxUSr9ZlZ4jAuxHC3oFPEBdwkiA0hXiiUhyaqbTbKJiaofaH1WTIOW60/x5P1+1X6eqDITWar+d+IhFw1P0YvEiWlQkyB2LpQnBIDMR1iF3luYpTaZmSRiBMzYCNTJCjjt4Y4QSgJD1bxY4VZsrAEtX1JvnwgX2yjSMyNUeN9BaKkCFV9sFN8Xn/8MBfsslDCTh7gEcpHRw/kIhCGhKpyx54LJcmJap4P0oLgBNVanCLNi1Pb45bCvHCl3hJiD3lhonotnlIAN6eKH8+SFsQlqeLEi3J4kXGqePBlIBpwQAhgAgUcmWAiyAHi1q6GLvhLNRMGeEAGsoEQOKs1AytS+2ck8JoIisAfEAmBfHBdcP+sEBRC/ZdBrerqDLL6Zwv7V+SCpxDngyiQB38r+ldJBr2lgCdQI/6Hdx4cfBhvHhzK+X+vH9B+07ChJlqtUQx4ZGoNWBJDiSHECGIY0QE3xgNwPzwaXoPgcMNZuM9AHt/sCU8JbYRHhOuEdsLtCeK5sh+iHAXaIX+YuhaZ39cCt4Wcnngw7g/ZITNugBsDZ9wD+mHjgdCzJ9Ry1HErq8L8gftvGXz3NNR2ZFcySh5CDiLb/7iS7kj3HGRR1vr7+qhizRysN2dw5kf/nO+qL4D3qB8tsYXYfuwsdgI7jx3GGgATO4Y1Yi3YESUe3F1P+nfXgLeE/nhyIY/4H/54ap/KSspda107XT+r5gqEUwqUB48zUTpVJs4WFTDZ8O0gZHIlfJdhTDdXN3cAlO8a1d/X2/j+dwhi0PJNN+93APyP9fX1HfqmizwGwF5vePwPftPZswDQ0QTg3EG+Qlao0uHKCwH+S2jBk2YEzIAVsIf5uAEv4AeCQCiIBLEgCaSB8bDKIrjPZWAymA7mgGJQCpaB1aACbACbwXawC+wDDeAwOAHOgIvgMrgO7sLd0wFegm7wDvQiCEJCaAgDMULMERvECXFDWEgAEopEIwlIGpKBZCMSRIFMR+YhpcgKpALZhNQge5GDyAnkPNKG3EYeIp3IG+QTiqFUVA81RW3R4SgLZaNRaBI6Ds1GJ6FF6Hx0CVqOVqM70Xr0BHoRvY62oy/RHgxgmpgBZoE5YyyMg8Vi6VgWJsNmYiVYGVaN1WFN8DlfxdqxLuwjTsQZOBN3hjs4Ak/G+fgkfCa+GK/At+P1+Cn8Kv4Q78a/EmgEE4ITwZfAJYwmZBMmE4oJZYSthAOE0/AsdRDeEYlEA6Id0RuexTRiDnEacTFxHXE38TixjfiY2EMikYxITiR/UiyJRyogFZPWknaSjpGukDpIHzQ0Ncw13DTCNNI1JBpzNco0dmgc1bii8Uyjl6xNtiH7kmPJAvJU8lLyFnIT+RK5g9xL0aHYUfwpSZQcyhxKOaWOcppyj/JWU1PTUtNHM15TrDlbs1xzj+Y5zYeaH6m6VEcqhzqWqqAuoW6jHqfepr6l0Wi2tCBaOq2AtoRWQztJe0D7QGfQXehcuoA+i15Jr6dfob/SImvZaLG1xmsVaZVp7de6pNWlTda21eZo87RnaldqH9S+qd2jw9AZoROrk6+zWGeHznmd57okXVvdUF2B7nzdzbondR8zMIYVg8PgM+YxtjBOMzr0iHp2ely9HL1SvV16rXrd+rr6Hvop+lP0K/WP6LcbYAa2BlyDPIOlBvsMbhh8GmI6hD1EOGTRkLohV4a8NxxqGGQoNCwx3G143fCTEdMo1CjXaLlRg9F9Y9zY0TjeeLLxeuPTxl1D9Yb6DeUPLRm6b+gdE9TE0STBZJrJZpMWkx5TM9NwU6npWtOTpl1mBmZBZjlmq8yOmnWaM8wDzMXmq8yPmb9g6jPZzDxmOfMUs9vCxCLCQmGxyaLVotfSzjLZcq7lbsv7VhQrllWW1SqrZqtua3PrUdbTrWut79iQbVg2Ips1Nmdt3tva2abaLrBtsH1uZ2jHtSuyq7W7Z0+zD7SfZF9tf82B6MByyHVY53DZEXX0dBQ5VjpeckKdvJzETuuc2oYRhvkMkwyrHnbTmerMdi50rnV+6GLgEu0y16XB5dVw6+Hpw5cPPzv8q6una57rFte7I3RHRI6YO6JpxBs3Rze+W6XbNXeae5j7LPdG99ceTh5Cj/UetzwZnqM8F3g2e37x8vaSedV5dXpbe2d4V3nfZOmx4liLWed8CD7BPrN8Dvt89PXyLfDd5/unn7Nfrt8Ov+cj7UYKR24Z+djf0p/nv8m/PYAZkBGwMaA90CKQF1gd+CjIKkgQtDXoGduBncPeyX4V7BosCz4Q/J7jy5nBOR6ChYSHlIS0huqGJodWhD4IswzLDqsN6w73DJ8WfjyCEBEVsTziJteUy+fWcLsjvSNnRJ6KokYlRlVEPYp2jJZFN41CR0WOWjnqXoxNjCSmIRbEcmNXxt6Ps4ubFHconhgfF18Z/zRhRML0hLOJjMQJiTsS3yUFJy1Nuptsn6xIbk7RShmbUpPyPjUkdUVq++jho2eMvphmnCZOa0wnpaekb03vGRM6ZvWYjrGeY4vH3hhnN27KuPPjjcfnjT8yQWsCb8L+DEJGasaOjM+8WF41ryeTm1mV2c3n8NfwXwqCBKsEnUJ/4Qrhsyz/rBVZz7P9s1dmd4oCRWWiLjFHXCF+nRORsyHnfW5s7rbcvrzUvN35GvkZ+QclupJcyamJZhOnTGyTOkmLpe2TfCetntQti5JtlSPycfLGAj34Ud+isFf8pHhYGFBYWfhhcsrk/VN0pkimtEx1nLpo6rOisKJfpuHT+NOap1tMnzP94Qz2jE0zkZmZM5tnWc2aP6tjdvjs7XMoc3Ln/DbXde6KuX/NS53XNN90/uz5j38K/6m2mF4sK765wG/BhoX4QvHC1kXui9Yu+loiKLlQ6lpaVvp5MX/xhZ9H/Fz+c9+SrCWtS72Wrl9GXCZZdmN54PLtK3RWFK14vHLUyvpVzFUlq/5aPWH1+TKPsg1rKGsUa9rLo8sb11qvXbb2c4Wo4nplcOXuKpOqRVXv1wnWXVkftL5ug+mG0g2fNoo33toUvqm+2ra6bDNxc+Hmp1tStpz9hfVLzVbjraVbv2yTbGvfnrD9VI13Tc0Okx1La9FaRW3nzrE7L+8K2dVY51y3abfB7tI9YI9iz4u9GXtv7Iva17yftb/uV5tfqw4wDpTUI/VT67sbRA3tjWmNbQcjDzY3+TUdOORyaNthi8OVR/SPLD1KOTr/aN+xomM9x6XHu05kn3jcPKH57snRJ6+dij/Vejrq9LkzYWdOnmWfPXbO/9zh877nD15gXWi46HWxvsWz5cBvnr8daPVqrb/kfanxss/lpraRbUevBF45cTXk6plr3GsXr8dcb7uRfOPWzbE3228Jbj2/nXf79Z3CO713Z98j3Cu5r32/7IHJg+rfHX7f3e7VfuRhyMOWR4mP7j7mP375RP7kc8f8p7SnZc/Mn9U8d3t+uDOs8/KLMS86Xkpf9nYV/6HzR9Ur+1e//hn0Z0v36O6O17LXfW8WvzV6u+0vj7+ae+J6HrzLf9f7vuSD0YftH1kfz35K/fSsd/Jn0ufyLw5fmr5Gfb3Xl9/XJ+XJeP2fAhgcaFYWAG+2AUBLA4AB+zbKGFUv2C+Iqn/tR+A/YVW/2C9eANTB7/f4Lvh1cxOAPVtg+wX5tWCvGkcDIMkHoO7ug0Mt8ix3NxUXFfYphAd9fW9hz0ZaCcCXZX19vdV9fV82w2Bh73hcoupBlUKEPcPGuC+Z+Zng34iqP/0uxx/vQBmBB/jx/i82wpC3azj0fAAAAIplWElmTU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUAAAABAAAARgEoAAMAAAABAAIAAIdpAAQAAAABAAAATgAAAAAAAACQAAAAAQAAAJAAAAABAAOShgAHAAAAEgAAAHigAgAEAAAAAQAACI6gAwAEAAAAAQAAA0AAAAAAQVNDSUkAAABTY3JlZW5zaG905MkqaQAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAAddpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDYuMC4wIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZXhpZjpQaXhlbFlEaW1lbnNpb24+ODMyPC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjIxOTA8L2V4aWY6UGl4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpVc2VyQ29tbWVudD5TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KBOmy0QAAABxpRE9UAAAAAgAAAAAAAAGgAAAAKAAAAaAAAAGgAADIO0JF6WUAAEAASURBVHgB7J0HvBTV2cbPDVVQUZBgRWINamIBWxQFa0QUI0aJ5hOwgL3E3kuMqElMRLCgKCKiYsT64RdbsBfsDQWxIGBDbKAiQr55zuXdnZ07u7N77164u/M/vx/MnXZmzn/Oec7unmfeU9O1a9f/OhIEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQOoI1GAcSd0zp8AQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDAE8A4QkWAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACKSWAcSSlD55iQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAYwj1AEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCQUgIYR1L64Ck2BCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQwDhCHYAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIpJYBxJKUPnmJDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABjCPUAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJBSAhhHUvrgKTYEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhDAOEIdgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAiklgHEkpQ+eYkMAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAGMI9QBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgkFICRRlH/vvf/6YUD8WGAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAClUugpqam4M3nNY5gFinIjZ0QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgogjEmUhijSMyjejf4sWLfQH1t/wnijuiTMKmEltnP3yoH7QP0wNTRltHH9AH9AF9MD1AH2oJGA/0EX1EH9FH0wP0EX0UAasP9A/0D/QP2faAPqKP6GO2PdA/0D/QP2TbA/0D/QP9Q7Y90D/QP9A/ZNsD/QP9A/1Dtj3E9Q/67elnP/uZ/w1Kf4dTHeNI1DQSPpi/IQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQqk0CceSTHOGKRRBYtWuRL6CONLIkwknnziXUfcQUetZFn4AAHdCKIyIQuoouKzEU9oB5QD2gH6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADrQxHVAZpBmzZp5T4jGt5QyxhEzjWhpU9T4I/gPAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKAqCFjUERVG5pFY44gijvDmNJEkiCRBBAF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdKC6dEARR/RM8xpHFG1EDZ8EAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFBdBGQaUdQRJf3tI46EjSKKNoJjDMcYjrHqcozxPHme6Dq6jg6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA6YDijpiKdY4YjtZQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhUF4FE44gcJpYyjqNgg7baOvtrCRgPzf4DH+qH1QfaB+1DBKw+oA/oI/1Dtj2gj+gj+phtD/QP9A/0D9n2QP9A/0D/kG0P9A/0D/QP2fZA/0D/QP+QbQ/0D/QP9A/Z9kD/QP9A/5BtD/QP9A/0D9n2QP9QYv8QTE+TaBwxqCwhAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABKqLQKJxxOa0YckcV5mICYHjiPpAfaA+0A7QAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHah8HchrHFEDX7x4cXXZZCgNBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgECGwM9+9jMnA5BSTdeuXQO/iGY/CuaAWmIc0RKHUOU7hHiO1GPaMe0YHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0IKoDicYR7yLhPwhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBqiOQaByJOk1Yx4GGAw0HGjqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6UB06kGgcqTqrDAWCAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEPAEEo0jcghZyjjGgg3aauvsryVgPGrgQ/2gfaAPNbUOU/QRfRQB+ocljuuABZ8fsvWhtnVk1/n8QP2gfWTbA+2jlgD9B/2Hf2OH/pPPD0EdMD1AH9FHEbD6wOdHPj/y+THbHtBH9BF9zLYH+gf6B/qHbHugf6B/oH/Itgf6B/qHgv1DMK6ZaBwxUWUJAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFBdBBKNI8xJVB1zEvEceY6ZN5KWRMJgnTnn0AV0AR1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1ABxKNI9Xlk6E0EIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJGINE4gsMMhxkOMxxm6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6UI06sNFGG7lPP/3UzZkzx5VSPv2wtvLKK/t/ixYtcrNmzXI//fSTo5003XbSuXNn/4xnzJjBc/pv031OpbRD2hvPkfrC51N0AB1AB9ABdAAdQAfKpwOJxhFzmLCEAAQgAAEIQAACEIAABCCQNgKDBw92+tL07rvvuocffrjsxdcgVu/evX2+Dz30kJs+fXrZr0GGEEgjgc0339xtvfXWRRddA/9z5851n3/+uTcQTJs2zS1cuLDo89NyYNeuXd2OO+7oxo4d6+bNm1fxxd5yyy3d+eef7w477DD38ccfJ5ZntdVWcz169HDbb7+9W3/99X3/YCfpB2sZUGbOnOmmTJni7r777qpgZOWrhmXPnj3d8ccf7wYMGOC++eabaigSZYAABCAAAQhAAAIQgAAEIFA2AonGEZxaOLVwapXPqUV7oj3RnmhP6AA6gA6gA+hAZenA//7v/7rmzZu7+fPnuwMPPNB9//33Jb2RnvS8TzrpJLfbbrv5L3gXX3yxmzRpUlnzT7o++yurPvK8in9eaq8DBw705o/Fixcn/ojSrFkz39btwK+//tqp/eufzCT037X995577ukH3v/nf/7HmyQqncuIESOcTEJXXHFFwQgUK620kjvkkEPc7rvv7uvC7Nmz3bPPPus+++wzbzRSP7HGGmu4tdZayxtK9LeMNePHj3d33XWX+/HHHwvmX+kcK+X+9SPoVVdd5V588UV33XXX0a5r+FxOv1p8v1op7Zz7pF3TrmnX6AA6gA6gA+hA/XUg0ThiP5qwhAAEIAABCEAAAhCAAAQgkDYCGjT+4Ycf3PLLL++GDRvmB5HLxWCFFVZw48aN8wOKyl/Gkccee6xc2ZMPBCqKwDnnnOM6duzojjvuuLLc9x/+8AdvHDnrrLPcCy+8UFSe7dq18/egqUv22WcfbwRQ1JEzzjjDvf7660XlUe0HKUKSIjbIOCLTRDSJmwwWMu409Ygk2267rTv77LPdoEGDYstiZdtqq63c6aef7tq2beveeOMNd80113izie2PLvUjpaKSHHzwwd5IomlRTj75ZCczEimewO233+4eeeQRN3LkyPgDyrh1m222cWeeeabXB0UZIkEAAhCAAAQgAAEIQAACEIBALYFE44icWZYyDp1gg7baOvtrCRiPGvhQP2gf6MOSN3fQR/RRBOgfljhcAxZ8fsjWh9rWkV3n8wP1g/aRbQ9NqX3IOKK30TWNhQYNhwwZYrfXYH3fb7/93OGHH+7fRt933329cUQRRyzRf9B/+DelggqRBn3485//7E0bRxxxhG8CDa3/FnHkrGCAeHJgHLH8im1f+rFE0SVOOOEEb4DQ8qOPPrLTM/mlrf+WcUQsMhFHIvVTuqYpvvr16+e+/fZb11T5WOSJt9580w278srM87QHbPVlp512cqeccopTRJoJEyb4KBXqD2x/ofLpmP4HHOAGBsaU9957z+cjJkrFnJ+m9n/PPfe4f//73z4ayNLgIyOo+vbhwbNPg75affOVj/pH+7NIM0FdoP5n9Zj2UUvA9KJQ/5am/sl4UD+oHyJg9YH2Qf9B/5ltD+hjlelj8Dkx0ThiD50lBCAAAQhAAAIQgAAEIACBtBEw48jdd9/tow7orfFyRB7Qj0433nijmzVrlnvuuefc0UcfTcSRtFUuyptD4MILL3Q///nPnRlHcnbWY6U+EUfiLrP//vu7Qw891L399ts+0kbcMWnalhRxRIYRM4405Ygj3bp185qraCOadiYubb755m7o0KFuwYIF7vLLL693RChFYDkgMJBoehRFuiDVJaA+VsaRq6++uu7ORtjSq1cvd+KJJ/rnoinoSBCAAAQgAAEIQAACEIAABCDgko0j3kFqTmSWzEkbRKDJOCupD9QH6gPtAR1AB9ABdAAdQAeqXAfMOHLSSSe5W265xb322mt+sLGh35O6d+/u/vKXv7jzzjvPD5abcUQRR/i8Xf+5WBv6XDh/2X3fs4gjRx55ZFl0NRNxJJiqZvLkyQ1qVxrMXnvttV3fvn2dpq5Jcz3Zc889M1PVfPrpp3W41ok40kQ/J5x22mluzTXX9FMjxT1PTSV27bXXug4dOvgIKzIOxR1XrF7rejvvvLOfGuf555+vw63YfKr1OIs4orbWEM7F8mndurUbP368GzFihDesFHsex9E/L436ST2jnlHPlt3nUdof7Y/2R/tDB9CBtOsAEUewD0EAAhCAAAQgAAEIQAACEMhDwIwjmpphwIAB/u1kTdHwxRdf5DmjuM0XXHCBW2eddXyeffr0IeJIcdg4qooJNNWII0J+3HHHOTNMyECQ5lQNEUdatWrlbr/9dnfTTTf5qcLinqc980cffdRdeumlcYeUtG3VVVf113v33Xe93pd0cgoOXtoRR4T09NNP98YgTUVEggAEIAABCEAAAhCAAAQgAAEijiyVNxlwqOFQS7tDjfLj1EUH0UF0AB1AB9CBStUBM44cf/zxPjLImDFj3Lhx45yW9a3XNoCoQUvlpSgG9Y04ojcBFi9eXLbvNfqSrHIpz/qWL3ye/V2pzz98382aNXOLFi0qCxfL136UsPVilvbMjW1DlpZXMdct9jrKU/kplZJvU444sssuuzgNLl955ZXu/vvvL7pcxqwUDnZO3NK2lSu/+uRjBhoZ6Boj4khjtLNoOTVNiSKAaDqjL7/8ss7zXHHFFd3YsWN9iF5NU/TJJ5+Upd1rupuNN97YaeoaTVMWva9862pLevbl0uV81ynHdtMUq6vFLhsScaS+fLbaaisnw9rBBx/sPvvss6KfRzk4FcuF4/j8TH3jezQ6gA6gA+gAOoAOoAPowNLUAX2n0/X896yuXbsG9S/7A4++lJIgAAEIQAACEIAABCAAAQiklYAZRxRxROncc891wfcmp0HTn376qV5YBg0a5DSdwx//+Ec/aLn33ntnjCOPPfZYwTw7derkNIj9m9/8xulvTafw/fffu88//9xPx/F///d/bsaMGQXzCO9cbrnl3D777OMHM2VoUZ76kqiIKsrzrbfechMmTPD3GT4v7m+9xb/NNts4DcpqWo/27ds7bdPArAbl9Kb9nXfe6WbPnh13ut8mFkr33nuvX5by3xFHHOFeeuklp2kgLOnL7pAhQ9yLL77o+dh2LX/1q1+5DTfc0HXs2NG9+eab7vHHHw/vdm3atPFl6dmzp5/SYuWVV/bGEbF544033MMPP+yvl3NSZEWD7JoSQeVW0v1ouorddtvNde7c2bVr184Pvs+cOdM98MAD7qmnnork4Py1+/Xr537961/7e1UeOv69995zt956q/+7zkl5NjS0/uTjqcFwPbt1113XqR7ptwQ9Zw2O/+c//3FPPvlknTvSdE36Z2nbbbd1qo+K8BBO4vLhhx+GNxX1t0wBAwcOdGcFU9W88MILRZ2T76Df/va37sQTT3R/+9vf3EMPPZTvsLLUGctcrFVH1d5/+ctf+sgIyy+/vJs3b56vM2J73333+emz7Jzocsstt3TdunXzU67Ybz3RY/Kt63lKCxQJIpyiEUdWW201b36zY9Zff323ySabOGnnjz/+aJvdq6++6p555pnMuv4oRxlzMixyRSYltct8kSas7kj7NF1NudIee+zhNBWT8hSffKlly5Zuxx139P9+8YtfOGmPnt9XX33lpk2b5tRPPPHEEwX7oHxtNd81w9vtmcokJa2xtMYaa7i99trL1zvVPyUZfbbffnu3++67e636+c9/7r799lvf/j/66CNff6T90aT6tfrqq2c2K1+1c00HF06jRo3y00OFt6200kpOuqzrStPU16i8c+bM8W3j5Zdf9n2I2kqh1Lx5c/evf/3L3XzzzRmNLnQ8+yAAAQhAAAIQgAAEIAABCFQ7gUTjiL6c6gsYSzhQD2gH6AA6gA6gA+gAOoAOoANp0wEzjijiiOr/Zptt5qct+Mtf/uKNBqXy0ECVooy88sor7uKLL/bfs4qNOKJBslNPPdUPymu6jHfeecebBzSYJzOL/mngfcSIEW7ixImJ3+M0cCcDTNu2bf3A/gcffOAH+vUlUYN/a621lh+41JfiG2+80U/pkK+86623ni+P3tR//fXXvXlj7ty57rvvvvNmBw1EylAio8Qjjzzi/vGPf3gTRjQ/DcyL85/+9KfE+4/q0YMPPuiNFKNHj858f9W9//vf/3a33HKLjxKj62mAU8YdmVu0rsFF7b/rrrsy56nsFgFj+vTpzniL1UYbbeRZyyChgftrrrnGD2xG70frqicqsyLKaIBTeYqFjBQaAFbEBq3rnnQ/GjSVOUncdL4MCzItyQykgXeZRTQo26VLF6dBaOWtyDXjx49P5FWO+hPlKSPD2Wef7Q0OKo/qpJYyDMiQs91223lTkurEJZdc4s1IxkllloHG1lWmFi1aeINRuF7ccMMN3thjxxW7PPDAAzPGkcmTJ2euU+z54eP0/NRepAOqC+H7s+P0/DQFldqO1RkdK0Zqm6o3qjMajL/qqqti67/lK0OY6o6MTXr2qi+KevH11197E4GuIZOWpruS6UlaokFzO9+WiqRw0EEH+fqlZ2fbi1mqLUqvZJgJHx+NOKK2LzOEcdBzVzmnTJnijQ22XWYHGcJsXXX3oosuyinjxx9/7Ou39imPrbfeOlPGoUOHegOanV/fpfRNbV2mELWduHwUWUYmqP3339/rQ7j8cceXc7/KLQ0QVxkvxFFGNRk0ZNDSP2mGTH2KlhEXMUX3o3LKSChty1fOfPctw5Kev6ZykQnDjtt0003dZZdd5vsh6ZHq33nnnefruNq+9UkyvsgUIpOcyqP7EFOZLY3fUUcd5RnbusqlsqgO2PW0lPHrhx9+yJwnTVQ7VF7PPvus56C+RsdKY9UOewamEu1XZLCwrofzteuqnNJUsYzbb8ex5PMv9YPvwegAOoAOoAPoADqADqADadCBROOIflwgQQACEIAABCAAAQhAAAIQSCMBM45YxBF9Sbz++uv9m98nnXRSyUh22mknP0XCySef7A0WykAD0jZVTb6IIxoE1yCw9st0oYG0aNJgnQbUFKFAA3XDhw+v86a2nSMTwTnnnOMH3mR80GBdXNKb7scee6yPcKIBPEXuiCYNbl966aU+SokGPO1N9Ohxuj+9VX744Yc7TUtw9dVXRw9xf//73/22+rCVQUQROGQcsaTnZYOnGkA95phjnCImKFqI7kFGjGjkGEVpOPPMMz0TGT3ysVGklsGDB7upU6f6AXAN3EeTBvRlphEXTVOhwf8rrriiTp66zwEDBvipMzQYqsHYHXbYwdcVGY1UrmhEUA3qn3HGGf6te027ITNSvlSu+hPmqbYhM4gGtMXp/fffj728DC5muFC9j/K2kzRwK0OEIseUI1nUiHJEHFGb0yC58pSpJ5rKWWdkGlFdkQFD9SccQSd6XU21ITOZ6qjMVgsXLsw5xIwjGmzXj5ylpL/+9a8Z40j4vGjEkfA+/a3oOGoXWuaL+FDOMkavn7QuY8HIkSN9G1NbiyaZ72QqUcQcTVOzNNMWW2zh27SiSEkD8rUpaa5MSnqmMt/IPBRN4bYqA0UpSdGN9PylL4riZMmMk9Ib6ZHuQdeW/st8EU26B2n5rrvu6iN6iHu+pMg20vC4fsHO6dOnj++Pnn76aX9/cW1RxyoiieqgjCvSlbhITpanjpOpUW2bBAEIQAACEIAABCAAAQhAIO0EEo0j4R8X9KXPO6oCavrJwdYNoq3XsB8+1A/axxK9QB9qCaCP9B/0n4Ejmc8HfD7g8wGfDyrw84EZRzTwbf25DAN6W1oD3DIelKJvGoBW1AqZJyy/vjKOBIYGDRJPmjSp9sNDqL1oShtFBpHBQ29PW7Lzo9e3+1P4ff2L7l9llVX8wKkGm2WI+SkYbC70/a51MN3MlYEJRVEkZHCxZNeXGUDTjBxyyCE+IkJ0f/T6mqpHg3SKVKFB8fB+vf2tpMFGyz+8v9D3U4s4ougoSjpfySKOaGoKDXprQFQRHOLy14Cp3qhXVJRhw4bVvuke5JGPj97MP//8832ECQ3gR+/PIo7IXKLBTA20WopeX1/OtV9RFjSFhgZnr7vuOjcpmOol3/VlxvnnP//ptFR9DBsHLP9y1h/du/HUVDuaimhoUG/nL4mQEi2/ff7Zu29fX3dUhzUor2T3Z8/XIryYcSS639b9yTHnR/dnIo4EJqDJwVQ10f22bte39Wj+vwnq9vnBc7H2FN2/WRCF4dKgzmiKHT2LBQsW+EMsv2j+qmOqM9IOtZ3ofg2y6/mrPskMFN0fvf6WwXQ/FwXRSe644w5vagvvN+OIorso5Xs+cfUrE3Ek0IjwfrUh6YY0SRFzovenaD5mHJGZILpf92FlVDlfCyJXhPPXfqUwv25BGdWWwmUM7086P7xfLKQv/fv3d4pUEb0/Te2j6CaaVkfmjej+2rvLvb9w/vXdr+mIZCpTlBpdV9FlrP3E5S+TlUwRaodqM4pOEn6++SKOFHN/qqOZiCOBccSubxFHZDo87rjjnDT35sCUsjgwsNjziObfLNC10wMDiqbeUb9p09bY8cZXRj5pi6LxKEX3a10GGJnvpI0qayE+MtbJ2Kapk2TKM57R+9spMI3o/qw+R/fb/dn9sL+WgPGAT/7PB4XqJ/z4fYj6we9D6Cf6aZ+vpAeW6B/oH+gf6B+Wef8QfO9KNI6YaLGEAAQgAAEIQAACEIAABCCQNgJmHLGIIyq/jB+KAqGBYg30FZs09YEGxTSVjKZssFQo4ojezNeA9RNPPJGJxmHnFVpqYE8DoDZgFj5WUT8UeWPIkCHug2B6mmKSzAcye2hanWjECEXysKlIislLP4jJAKNIIDIShFNjRRyREURRVmRO0LQl+ZKMPYoQIjbRcuY7x55fXGQLmYEU4UBJzDUVQ6Ek04gGg7/55hs/EKwoEklJkUl0bQ0eRyMUlLv+6NnpucnQoKgHAwcO9NF3ku5R+zVdxvz58/3gcdzxTTHiiAbn1WYVOUN84yL9lLvOaFBcA/eaYqbYpKgT7du3r8PWjCNNLeJIOctYLCM7TiZARWrJx1cRcqT3YZOKnduYSxnWNO2YDHhmPkq6nvoitStNcaX2E07WVqXP5Y44oulr2rRp480j4Wvm+1vtSP2YdD+q+XZOUsQRTX0jU6DMWQ888ICdVnApjZIBUEYtGYHikphrOiwZkx5//PG4Q9gGAQhAAAIQgAAEIAABCEAgNQQSjSPe4RT8OMQy+wZFxvkHF+pF6M0a6gU6gU6ik+gAOoAOoAPoQPXpgBlHwhFH9Jy1rmlnFNVAg+HFtH8NRiokvp1j9UVmDJuqRhFHbLuWGpjfd999naJ0fPHFF0VdR+dpMEzRKvQW97XXXptznt7y19QaBxxwQM728HWj5dF9n3766f5+FKnE9ivSxX333eduu+02P6hn25OWmopj0aJFtREXQt+r9Ja77kOGiUL3E5e/RRzRVDW2X9/s9Ra7kgYmZQLIl69FGlDUD01tkO+46PYWLVp41po2wSKy2PU1GNk9iJag6Rd0fdueb9mhQwdvStL9KiqCDDnR60XXFclk/PjxfuBTUxmF95e7/ui+jOfYsWP9gHT4evnKpe0y42j6kt/97ne+zUTPs4gjRx55ZCKnQtexfDMRRwJTjcxCtr2YpaYrUbvT/cqMozohM0/0uptvvrmPamAD09H9+dY1vc+oUaM8BxmKNO2H3ZemmJJp6cQTT6xzvXz5ySC2//77+2mv1K7sODOOhCOO2HWSlpmII5H72HPPPb3+WYSGaD51Io6E2rfdlwbzVeaortr+uKU08Pe//703rymyTvS6xa6L71dffeWn6oq7jnRRU9TIECSDQrH5NuQ4TVGjKCdXXnmlu//++zPPL+7+oteRoU9sxHLKlCmZ+80XcSR6fty6IillIo4EJhG7D4s4Ih1QP/JqEC0m7nw7Pry0SCLSyPB2O98ijkgr4/bLVCeN0DRiL4QiCNn5cUtps/KV0VN6FZev2uLEiRO9lslkE5dP3HkcV32f93jOfI+jXdOu0QF0AB1AB9ABdAAdIOKIvu+SIAABCEAAAhCAAAQgAAEIxBIw40g44ogOXGeddbwZIDp9TGwmwUaLUmJToISPs4gVik6hgX9LOkcDWRq8lPmj1KQBNr1Zr6lrwkmmDE0hIWNJsUmDxZqaQAOqM2fOzDlNpoXPP/88Y5rI2ZlnZbvttvPmFp0bTo0VcUQ/AGmAtVDEDw2UyjBg5o/wfSX9bXxkApg2bVrmcD1TGVLE7p133slsL/SHmCjqieqFpgdKSvphQwOfGhhVfbHUGPVH11LEEZkTZKr4/vvv7XKJy549e7ozgikhNF3F9OnT6xzfWBFHZMaS2SkpaWoLRUZYbbXVnKIbaEoNRSCQUSZf9JnGqDOKHqPoODKCqJ0Wk9Zbbz1/jqYBksHFkhlHmlrEkXKW0cpa7FKRL2R40LOLS9I4sdc0QjJuLY2kKBqaQkxGr3x1Ld99SLMUUUT6oum/LFlbbYyII5pOR0aVUpI0UBogQ1ZcSoo4oil51GepjpfSH0qn1D8ViiZiEbBKyTeuDGyDAAQgAAEIQAACEIAABCBQ6QSIOELECN6oiHkDCmchzkKchTiM0QF0AB1AB9ABdEA6YMaRuDfjFTVD0R4OO+ywnIgBcfqhqCIaMLfpRML1K1/EEb3d/de//tWH2X/66adL/txub/4r8sKcOXNKPj9cDk0toQgrmkZh1qxZOW9uy1TTu3dvP22PeIXPC5ezmO0azNVx5Y44okFVDVzmux99Mdab/nERWoq5bw3cawqiSy65xP3nP//JXEcRR2Qc0TPW9BP5rh/efv3117tWrVo5DfqHtxe6D019JPOI3ta34xqj/ugHEBkpVAdUF4q9Px2nKDfikS9SQWNFHKnPjzYyRz333HN+iihFcYgrp4wmirYj9mHuxj9pucEGGzhFv1Cd0bRXdrzamaL7KIKODAXhCCJx92HnxS3NONLUIo6YiUh1SdFHGhJBJK7chTipjZsZL+44MdPUXDIyPfnkk5nnUup1ij1eES9Uj+68804fhabY88LHybAmpjJJ2PbGjDgirdE0bXH87PrRpRlyzMQU3Z8UcUScZNRUn3veeef56CqlXD96vfC6jDea7kf9bXh7ufInHz5PUq/4XokOoAPoADqADqAD6AA6UCk6kGgcqc+PLJwDAQhAAAIQgAAEIAABCECgGgiYcSQacURls+lb9Pa8QucXSjIDKHqApp+IpnwRRzTYKwPFQQcd5I0f0fOS1mVYUMQLDdRrUKw+SW+zy/SgN+H1Q0dcxJF27dr568g8oYgpGkh/8803/aBiqddsrIgjivRy2WWX5b0dRZnQFDeXXnqpH8TPe2CeHTJ6aOBTb/crooEl8Vf0CvErNumtek1LJPNAscmMIxpYtdQY9Ud1QBFHnn/+eT/Vh12rmOWvf/1rPzCrcr0cTH8RTY0VcaSY9ql70Y8jK6ywgh+Ylulmjz328JGFZOpQ3dGPPOHUWHVG1zj11FPdzjvv7KZOnepuv/12P9WOjEelJjOO2GB9KedrEF3mmKhmySQmI52mqvnss8/qZCnzwuDBg72JYd68eXX224ZyldHyK2ap8kjTZRQIR+cJn2t6fOONN/opuML7GuNvq0fSPpmF6pM0/ZNMiZrKxyLOWFttjIgj6s/uuOOOkm5VZhzVGdUfmaGiKSniiI7faKON3Lnnnuvbqa4vXf/oo4+iWZW8LuOXopIobxIEIAABCEAAAhCAAAQgAIE0E0g0jlSKA4b7xLGHYw/HHjqADqAD6AA6gA6gA+hAuXXAjCNxEUdatGjhpwdRNAsNOOWrf5tvvrk3JGjw+eGHH65zXL6II4rooAFBmQHqUy69mS3Dgt4MLxQJRMaPNddc0/9r3769HzjXlB3apkFNDURqagJNnxEXcUTl1j8NWmpgsGXLlt74oOkgZCB566233Hvvvee/dyeVo7Eijmj6l1GjRuXluMUWW7ihQ4e6cePG+QHEpPuM26/IM5MnT/YRJGy/ImyIr6a/yVc/ott1nzNmzPARDyyfpKUZR8KRLxqj/ughKkqEpnYotV5aBBRNV/Piiy/W4dFYEUdkHNFziXJOWm/durXTuVtvvbWPBjFy5Mic+tO9e3dvmFKdkYEiKb+4/TJiyXQmg1F0/7bbbuuOPfZYP4WJpgSS+cvakzRH0xgl1QszjjS1iCN232KrSECapiVcRpVTUz798MMPdbhEOZWyrrYoLVB0ILUZu4/wUpE7NM3XQw891CgRKKSxMrDIqKDrajqxiy66yBt0VO5SymP3rWg+ykMGR4uQ05gRR84//3z3zDPPxPLLd//qG/RP03rFRZhJijhi+WoaryFDhrhdd93V9ykffvihbxvqZ8Tv008/Lem+lK9MUopoIpOUXYclnyetfbHk+yV6gB6gA+gAOoAOoAPoQJp0INE44r+J8R8EIAABCEAAAhCAAAQgAIEUEjDjSFzEEeEYMGCA+8Mf/uCXGrCKS+ecc4771a9+5SOHaMAsmuwNdw0eK2KHJQ1ay6yhSCUNSWPHjvWDpOE8ll9+eT/1jAaUFSlESdPZaDBz7ty53vjx8ccf+ylJXnnlFdejRw8fuSQu4kg4Xw0AazBYZpnNNtvMvxmu/d99950f0NSUOxqQzRc9obEijiS9db/XXns5Tfcg1osXLw4XqaS/FUlDBhRLeqYa6FTexSa9zW/GkWLPMeNIOOJIY9Qf/ViiiCNJPOPu2yKOyDjy0ksv1TlkWUccqXNDwQb9YKL70sC8ov9oYNpSY9UZy19LRfyRsUDtSf9WXXVVv/unn35y7777rjfgSKMUoSYumXGkKUYcsfttaBktn2KW4nfTTTe5f/zjH74ex52zxhpruBtuuMGb5WQYLHe67bbbvBlPUVmUTP/79+/vvvzyy3pdTiY/Gc5kgJA5Uakx2qo0XVGZNHWZ+oVSkkUckXFE9Teaiok4Ej5n3XXXdTJvqV1svPHG3rCo/WoLaqeKRqLppvQjf1LSlFDqk1QuEgQgAAEIQAACEIAABCAAgTQTSDSOhL9kZRw1ATF99bJ1A2jrNeyHD/WD9hH8qIx+ZH+kQh+XOJPpH+gf6B/oH+gf6B9DgziV0D+acSQccSSQMp90/zJKaGqSf/3rX5mIFuH9HTp08PvvvPPO2P36vNR3773d0YGxQCaDSZMm2el+EEtRGg468MCy9h+dOnVyiuzRpk0b95///MdNCv69O326HzjL9/ltl+Dtbk15oygWM2fOzJRfx+f7/qcvm126dPGDeppiQAN8etteEUw0VcR9991X+2Z4kJt9v9R9KZ100kkZvcyXf7j+1ATXkqHh1ltvdZpiQkn7lRQhw4wO+cq3+29/640BitAye/Zsf144f7u/fOfn2x8XccRnHvyXL/86EUeCY/Plb/zvDVhOnDjRKeKIJQ2Clrv+KO8oz2Kej+7fIo5oqhoZR6Llt4gjmnJDKbrf1v3OIvYfGLQbPc+zgugRk4OoHqWeb8fvuMMO7qyzz/bTlshQYElT2ShCgdUZO97223qxfOz4QucrEpAGyfVPRpy1117bD8JrihNF0Qgb05SfGUfCEUcK5R+u35dffrk3zpwYRLEI1z9NNSIjnaJHyCwXLd9+++2XmapGRqzo/qTrS5/ylXH48OEZ04HxKiX/tm3bugkTJvhoOYqaY+0nXD7dn8wdipy07777lnz/hcpnphSZJNRWdf2ddtrJT0t1+OGHO0XPsFRK+boG+irzg0yKMksoSX/N5CWzjOVXTP7SahlE/LRSQVs1PmrDipylaYYUUSofP13DrmfPxyKOqP7IOBLdbxFHrrrqKn+L0f22Hnf/LYJnteGGG/qpbFR3ZHBRxCBNYzMsiLj1ahCtp9D51wemmw8++MBJgyzZ8Xb/ts7+WgLGAz7J/bO1n7C+wo/fh0rRT6sv6A/6IwJWH9Bf9Jf+Jdse0Ef0EX3MtocG9w/B7wiJxhFrdCwhAAEIQAACEIAABCAAAQikjYAZR/JFHBEPTVNjEUU0fUQ4aeDWBrA/+eST8K7M3/bGeTTiiAbaDjroIP9GejTfzMkl/iEji962X7RokR/8U4SRYtIuu+xSVMSRQnlpega9ba4ILSuvvLIf6HzggQdyTqlvxBFNqTN69GhvHNHSkn5ctcFTmVXyJYuGoSkqNI1KudKyjDjSGPWnWJ5x/IxxJUUcUTlksLrjjjvc+++/nxM5xspT7joTxy7fNkX3UdQjRV9QhAVFnAgPTppxpD4RRxSpSPogc0w4aeBfRjrVL03RE039+vXLGEfmzZsX3V3yelIZS8lQ9Veao+mFCumBTFcydFxwwQVOUZLKlWQ2Ul+iPsMMHhtssIG78sornU3/Up9radqWk08+OTOVmPJoSFuV0UgRdqJttSlFHCnESVMSKYJLnz59fF8nNorQky/dfvvt/jlrWjcSBCAAAQhAAAIQgAAEIACBNBNINI54B2zw5ZolczjphwfqAfWAekA7QAfQAXQAHUAH0IE06YAZR8IRR6Ll32KLLdwll1zio3jozX/b36JFCx9tZHoQzUPThtj26LJv377u6KOPzkQcsf0aDFSUD3sT3bY3ZLnPPvs4RXTQgLIGfYttzzYthyKOzJo1q+jz4vLXm+B6m11viCs6wfz58zP5aeC7WbNmfrC6lHJ269bN81PEERlH7Lr6sl8nQkbM95qOHTs6DZRrAPf+++/PnG/51HcZF3EkqVx1Io7E3G/0fmyqGkUxsPwbo/4UyzN6f1q3iCMajJY5x+7TlhZx5MgjjywLfzNsqe1Nnjy5zvXsusUsFcVG0zupvtrxioyhaEOKgiH+tn1pL/WjzpAhQ9zvfve7jCHB+Nv0IDJ7yCxm25OWirahMk2ZMsW3xfDxMn+ZccRHHInUzzoRRyL768NH9U66pTKed9557plnnqk3b0WHUqQlRbYIlyt8X4paoYgrilghtjkRMhpQHtV9Tfslc83333/vr68oKIp+oimqxo8fX69yyTy0//77O+m0ptqyckn7LApTuHy2P99y0KBB3njhI44E02/ZcbERR4rkYRFHVH8UGSd6PxZxxCKxRPfXZ12GQrVPmTbVx1o5okv18xYVrD7XiebHOp+TqUd8X0YH0AF0AB1AB9ABdAAdqFQdSDSO6As6CQIQgAAEIAABCEAAAhCAQBoJmHGkUMQR/SCgQb/vvvvOHXvssRlMO2iKi2DQOvx2eWZn6I98EUc22WQTpwgcMh88/vjjoTOK+1MDdIMHD3aHHXaYjxygs3Qva665pt9eXC61R2nQTfd56KGHZqaq0R5x0dvdejO/lPTLX/7S6e3uaLQGlVXT/2iwtpRkDM04Yufq2RQTcUTHyTCiaAQaaCw1de7c2Z+naC4alLa0LCOONEb9KZanlT+8tAgd0SgGdsyFF17oNBWLTVVj2+u7VGQbTSOjNvhCMFVNQ9I111zj1lprLR8xx/JpjDqjiBSaHkXtSoaqYpOi+SgqisweNlWTzpWZQG3297//vZ8iqtj8unTp4q699lr31ltvlT3iSLnLWGyZ7DhNN/TOO+9485pti1vKRKZoIMOGDXPqBxqapHnSB03xIkNGOGlqnKlTp3p9Dm8v9m/1E9JhaX04aTow6Z+mMSolacqb7bffvslEHFFbky4/8cQTfhqhUsqiNiAdkOlowYIFdU5dbrnlnKYOGjlypDeP1DmADRCAAAQgAAEIQAACEIAABFJEINE4UqmOGO4bR59+XKAeUA+oB7QDdAAdQAfQAXQAHWiIDphxpFDEEeWvqCFHHXWUN45oAFD17m9/+5tTVAK9Da63wPPdR76II61atXI33XSTmz17tp82IN/5+bZr4FPpuOOOy3wulrFC0Rc00JjvvOh2RU5RNA5NLxONOKIBuQMOOMC/QS/jTLHtTXnKqKFBXE0TYOdpwFwDlopaoGTbk5ZiremCzDhixyuPYiKO6HhNFaGpGPRm/Lfffls0H/HS4LwG/DVIGY6gsiwjjjRG/SmFZ7QeVXLEEQ34b7TRRt44ougTVr8U/UJ1RpE9ws/d9hdaapBfdUbtx+rb5ptv7qMXaeD++eefz1ynUD7GWUYstUGZsez4Xr16eZOCzDiaase2Jy3VBlQmM46Ej29oxBFFBxo6dKg3STz77LMltbN//vOfvowyH1m5S13q2mobJ510UkEeiiSlY7/66iuv4TIdhDmUcl0ZexRJQ/2B+glFMgmfLx2V0Un7FKGqlOuo/l122WWxEUtkIvrggw+8toWvVyj/FVdc0UdtUoSdphRxRM9e/cYxxxxTEp8tt9zSmy/Vt6g+Rzkouozat8r60ksv1dkfPZ51vlcUaj/UD+oH9YPvnegAOoAOoAPoADpQ6TqQaBzRD0MkCEAAAhCAAAQgAAEIQAACaSRgxhENOhVKmm5g3Lhx7qmnnvKDeGuvvbZ/g1kDd3qbvFCyaBmKTvHYY4/lHKqpZTR1x6mnnurfVM/ZWWBlvfXW82+ZR9+iVhQBhe3XgHexaZdddnEnn3yyH1CLRhzZdttt/aCkokWo7MWmX/ziF05RHDRlzaOPPpo5TW+Fa5Bbg7pvvPFGZnuhP7baais/pY8GPM04YsfrR5tiIo7o+HXWWcdPX6HnOGbMGMsicampd/ScZRiKcl2WEUd04+WuP6XwjIJLijhy2mmnOQ3iahqlcqRyRhzRFEq6f7XVcNSCctcZDdZrKhVFDtFUKsUmPRcZsDSFiwbBLen+ZFiQAU31upgkg5jqswwyMjiceOKJOadp2hubqkbTXUWTIopIL/UcNZVNNJW7jNH8k9Z1X5oqRm1DP+gVSjY9jrhKqzS9TH2SdFOmMkX+0LONJvUfekZvvvlmHQ2JHhtdl2lOU7LI1BeumzpOeiQzkq79448/Rk+NXdfUaLvttpuTnkajA8mkIg5qq6+88krs+fk22rRJMh6pbkXTLbfc4l4OpsVReeKSTccko9XXX38dd0jsNjN1HnTQQW7OnDl1jrHp22TikvGKBAEIQAACEIAABCAAAQhAIM0EEo0jle6M4f5x+OHww+GHDqAD6AA6gA6gA+gAOlBfHTDjSFLEEeWvwdJdd93VaYBK/zRAduCBB/pBrkLXzxdxRPVWb1iPHj3affHFF/6NaEU1SKrPGoTU9AoKwS/TyTfffJN5Q1tvVWsAXIN4ioJS6L50nS7BlBUyP8gUEp6qxs7TtTRNj/LS2/J6Oz/p/rRfg3+DBg3ySw0CWn4dOnRwGkBUlBANgNv2fEvxUWQVPSe9iW7GETteX/aLjTii+zr77LOdIg0oSsvMmTMTr6/rKMJDjx49vLnn9ddfzyn/sow4ovKUu/6UytOeg5ZJEUdsYHmvvfbyg9zF1KNw/tHj1fYUyUFT1SjKTnR/KetqA4qUoUFmmQfC19X0Txqcl0bMmDGjqOuoHWoqq1NOOcWbBcL5qc6oDsosFq1P4ePC929TP/35z392Tz75ZM79KarPokWLnKabknkgfF5cfn/605+c2rWmPlGkDBlHwsclRRxR5B9FNCoUweGiiy7yZYwrf777U8QXRZ3QuZq6K99xSdtlNFMeMkh8+OGHBfNR+9Gzl2ZKDzQll55xmEeh6zVr1szXQU0VpGgW0gqluPN1jO5J7KRZhfK1880YJi6aZsu223LHHXf01zSDnm3Pt5QBRebCUaNGeT3XM5SZw45XG1Z0EzMy2vZilopio3+qPwsXLqxTPuUr3qp/cfnJVCY2L774otfpfByj3BQ1RtOfadq2uHxlgll//fU9+7j90fxY5/Mk9YTvlegAOoAOoAPoADqADqAD1awDicYR/62W/yAAAQhAAAIQgAAEIAABCKSQgBlHkiKOCI293S/zgswgmmpCg1ZJqVDEEZ273Xbb+Te/NWWN3iD/+OOP82apwV4NxurNcEXteOedd3KOVV4a6J4wYYIfIMzZGVnp2rWrH2BVRJBXX33VafoM5f3aa6/lHKmBYg36KUKIBr1lHimUNHCrN9mnTJmSGUgNH6/71hvvMqTccccd4V05f6+xxhp+AFFf2DUoPnHixIxxxA7UvmIjjuicNddc079RL9ONBoxfeOEFy6rOUoPqMjsosoWip9x11111jlEeenNfppZik8qtwWlFcSk2KYqByq/7iKZy1p9SeYbvJSniiIwUMnkoQsbdd98dPrVef5cz4sj5wTRGiq6j5/3555/n3E+564zasKKN6Mca8XjvvfdyrhddUTuQoUGRPDT9zbx583IOUcQgtVtFzZCxRCaSuKQ6L5OIpvZQe9LfZhwJH58UcUT3P378eG82y1eHy13G8P0l/b3CCit4XVFki4cffjjpcNeyZUuvM1tvvbX74Ycf3OWXX14nMlRcJnouMl5ssMEGXhtlVvnyyy/jDvXbdB1prPRRUapGB4ZB/SAdl1Q3ZApU3yETzSWXXBL7XJWnopxoihyZ4qLaHc5bz12GEEW9Up6KsrM0I45IIxWtRvVO0ZvikrVpaa10Mi5ySfg8GThlVhHLfJG/lI/6IplSSBCAAAQgAAEIQAACEIAABNJOINE4Ev6imnHQBNT09dXWDaKt17AfPtQP2kfwIz36kf2hD31c4kSmf6B/oH+gf6B/oH8MDYRVQv9oxpFwxJFAynyKu38NKurNeO2TAcJHDAiOLvT9qW8w+Hd0MGgmk8GkSZNqMw/+D+e/wYYbesNHq1at/FQWGuiaNm2aD63fPHirvdOqq7ptttnGRzrReVdccYUfAPRvwkSuf+yxxzpFddBAsqa3mBYM0i0Mpg7QeUqaZue3v/2tfzP8vvvuc9dfd51bNXgTXdNXyIiibZqiQoOQln+fID9N66A3yTVAp6gHmhJHA3vKtXmLFt7Msv322/u8FQFCA6kaiI1+f1QZ9Qb9uuuu6w0u9wQmgo+DvHR/GgjVNDx6+1wRJT744AN3bjDY+s2337oHH3zQG0d0n0pWnmjEEb9zyX67//DzWWmllfwg6yabbOLuueceb5oRbzPEKCqKnvHAIJrF6quv7u6SCWfkyMzzCucfF3EkvD/u+nrb34wjcfvjzr83eCYyjsh0YUnlt/PLVX+Ud5Rn9PnFXV98LeJInUgUwT7tl0lBBg1F29DAtSIdqH68GkyJ8VUQlcbKky//6P5MxJEgysPkwAAU3W/rxdx//8AcpAg5ihhhZqLw+e1i6szbb7/t5s6d634WPIf2QZ1RnR0wYICvMzJuXRe0K3s+4fqn8ql+ySSmuijeMgYp4oV4KOnaMiTIzCLzwLdB/dcgv8xllsL3d3wQDUmGD7W7sWPH+oFy7de/Ll26+Lw0nUmbNm28WWX69Ol+yhBvHAnODd+f8pGRTgPymoomjl///v391CkynD35xBP++amtKsKHpWgZpSuq9zbdin6sUlvf7je/cXvFlDFcvvD9Wf6F9quNqX4NHz489v6VR/j8nwUau99++3nNkcFGJjlp9bPPPuvmBEaixUG/puOV9FwU6aNPnz5+2+hAjyYsMZXpeVsK52/3r30yJ+mf7k86+lYwfc37ATvx0LNSPVL+Wsr0cGcwtZGdH5d/57XWcsOCqEy6nqLPPPLII95cpHWZ2nS/MquoHukZyGgiM6CMNT7iSBApxfKPjTgS3LPtj7u+1Q+LOKL64/uF4Prh+i9dle5r6i/do8wj6k9Ufyx/8dG0aYrsJQOlpvd5PeiH5gQRuZSUnwxUMvnoGGnJ2JtvdmOCf8bbHxj8Z+WXyUn9tvqPQvcfd374/tnP7z/UH7XU2mTtwdq/rbMfPiJg9YH6UVz/abxoP7Qf2g/6YXqAfqKf9v2oUT5/Bt/TEo0j1imxhAAEIAABCEAAAhCAAAQgkDYCZhwpJuKI2PTq1csPtmmQdMiQIUXhSoo4YpnoLX29lf2bYCBVA7qaHkZT2LRv395pSgQNxt1///1u3LhxfnocOy+61LEaVNPgrqYm0JdNDXArrbzyyv5LoqZVULQPLS3JbPK73/3Oh/0XF01pEE4dO3Z0Bx98sFOEA33R1P3MmTPHD0brLX/90CHjiQYFdZ/5Ih8oTxlE+vXr56ey0UCtpgfRlDu6hvKWcUVRKZSPDTRrgN2mqrH70jVLiThi54mRyqJpFXTvSmKtQUkZW5QUUUYDt+HBcL8j9F9TiDhit1OO+lNfnrqHpIgjOkaDxprGSHV8rWDAW89ag9evBOaRUpNFJ1DUDjN7lJqHHb9hYNzS9E+KUKFIDHGpXHXG8haLfffd12kKExk61E5VB8VEz1LXk3FDEYHUFszYZOdHl4rocuihh7pVA5OZ2qbavPJRfVb7Ujv5V2BCUJtVUjnrE3HEriuDgPRQBhHdq3RpzJgxttsvy13GnMwLrAwMTF/SKZkZwj+2FTjF75LWSjc1NZX+VpIOK5KI8tE2PR/9LWOd6owMP6UmGTlkVFIUKyVNUaZ8pYVKMlXIeFQogog/cMl/ui/lJ91XG5bRSLqpKVyUpMvS+ycCk4ZSvraqSFaa9kbTu5TaJmWGEW9pqupfXJIRTwYdGT90z7pXRSGJPiNFwzrkkEP8fSofGapU/2WEUVtR/prSRmY6mXvyJd2LIrfomUYj9eQ7h+0QgAAEIAABCEAAAhCAAASqmYC+e+q7mFJN8OUr+D5W6xDWspg5r3WcMmAJB+oB7QB9gEsrAABAAElEQVQdQAfQAXQAHUAH0AF0AB1ofB2QeUFvm+sNbQ28ykShwUlF+PARPIr8fqZB4c6dO/uBZBkyNNimt7hnzZpVG0mgyHyi7b5t27bekKKpGjQw+d133/lB6o8++shHRIgeX2hdBhLlo38///nP/QCt7lEDp0vj+6oGvMVa19YguwbbxUf/NJhbifW9XPWn0HOrRC7lKk+LILqOzBKaHkQD2VZnFA1EA/alXkdtQGYPtYGw6UPt/t133/U/5hTLWz8AySymAXr9k2FEuqHoRfq72HzKfZy0SPdj96ZIEzJj6N40VY/aeqncCh0vloq+oahQih5SankEXc9Y0Tqkw2a4s2ctU5lMPqXmGz1e96nIS8pfSXkqGozqUqHyRfOxdbV9GbLEWXom45H6Dpnf6pOf5buslorII0ZWHplHVG9Un9XWku5LUVVkSNRUT5VY/qTysb/xP49Rb/jeQzujnaED6AA6gA6gA+hAtelAonHEfzvlPwhAAAIQgAAEIAABCEAAAhCAAAQgAAEIVAEBRQOR8UZLUvoIyCx5czCFzZ///Gf31FNPpQ8AJYYABCAAAQhAAAIQgAAEIBBDINE4Um1OGcqDAxAHIA5AdAAdQAfQAXQAHUAH0AF0AB1AB9ABdCC9OtC3b18/dYqmNFKEE/QgXXqw//77+ylqNDUWzz+9OkC7T1e753nzvPnch96jA+gAOoAOoAPJOpBoHIkxm7AJAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVSUDTtowZM8aNGDHCPfLIIxVZBm66fgQ0NdKoUaPcpEmT3I033li/TDgLAhCAAAQgAAEIQAACEIBAFRJINI7gwMKBhQMr2YFFO6Gd0E5oJ+gAOoAOoAPoADqADqAD6AA6gA5Ujg4o2sjuu+/uDjnkELd48WJH+01H+91jjz3c4MGDfcSZefPm8dz/m47nTvvmOdM/V07/THulvdJeaa/oADqADiw7HUg0jlShWYYiQQACEIAABCAAAQhAAAIQgAAEIAABCKSYwHLLLeduuukmN3r0aDdx4sQUk0hP0RVtRFFGHnzwQXfzzTenp+CUFAIQgAAEIAABCEAAAhCAQBEEEo0jcnZZyjh8gg3aauvsryVgPGrgQ/2gfaAPNbWOQPQRfRQB+oclDtmABZ8fsvWhtnVk1/n8QP2gfWTbA+2jlgD9B/2Hf9OK/pPPD0EdMD1AH8urj/vuu6/bb7/93KBBg9yCBQsMb4Y3n8+q6/PZXnvt5Z/1wQcf7BRthOdbXc/XGrDpJc+X58v3Kz4/mB6gD+X9/IS+oq/oK/qKvlbp+F8wrploHLFOlSUEIAABCEAAAhCAAAQgAAEIQAACEIAABKqFQIsWLdwOO+zgXn75ZTd37txqKRblyENgiy228AahN998M88RbIYABCAAAQhAAAIQgAAEIJBeAonGEeaSYi4pc46xXPLG45JIEvCAB/qIPqID6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6ECl60CicSS9nhpKDgEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABKqbQKJxpNKdMdw/Dj8cfjj80AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9CBeB1INI5Ut2+G0kEAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIH0Ekg0juC4iXfcwAUuOBJxJKID6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA5Uug4kGkfS66mh5BCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKC6CSQaR+SMsZRxSgUbtNXW2V9LwHjUwIf6QftAH2pqnZXoI/ooAvQPS5zGAQs+P2TrQ23ryK7z+YH6QfvItgfaRy0B+g/6D/+mCv0nnx+COmB6gD6ijyJg9YHPj3x+5PNjtj2gj+gj+phtD/QP9A/0D9n2QP9A/0D/kG0P9A/0DwX7h2BcM9E4YqLKEgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECguggkGkcqfS4e7p85xTJv4iyJAME6c4yhC+gCOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAO1OpBoHKkunwylgQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAwAonGERw2OO1w2uG0QwfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAHqlMHEo0j5jBhCQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQXQQSjSM4hqrTMcRz5bniCMURig6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOpBoHKkunwylgQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhAwAonGETnMLGWcRsEGbbV19tcSMB418KF+0D7Qh5paZyL6iD6KAP3DEqduwILPD9n6UNs6sut8fqB+0D6y7YH2UUuA/oP+w7/xRf/J54egDpgeoI/oowhYfeDzI58f+fyYbQ/oI/qIPmbbA/0D/QP9Q7Y90D/QP9A/ZNsD/QP9Q8H+IRjXTDSOmKiyhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoLgKJxhHmtGJOq8ybLEsiKLDOHFfoArqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6EB16ECicaS6fDKUBgIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDACCQaR3AIVYdDiOfIc8TxieMTHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0IKoDicYRc5iwhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCoLgKJxpGo04R1HGg40HCgoQPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD1aEDicaR6vLJUBoIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAASOQaByRQ8hSxjEWbNBWW2d/LQHjUQMf6gftA32oqXWYoo/oowjQPyxxXAcs+PyQrQ+1rSO7zucH6gftI9seaB+1BOg/6D/8Gzv0n3x+COqA6QH6iD6KgNUHPj/y+ZHPj9n2gD6ij+hjtj3QP9A/0D9k2wP9A/0D/UO2PdA/0D8U7B+Ccc1E44iJKksIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgeoikGgcYU6i6piTiOfIc8y8kbQkEgbrzDmHLqAL6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6kGgcqS6fDKWBAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEDACicYRHGY4zHCY4TBDB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAeqUwcSjSPmMIlbShhMHFRBSBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAClUMg0ThSyDG0/fbbu5133tndeuutburUqRkTiZlJ9txzT9e+ffuiaDzwwAPuiy++cIWup3xXWmkl16dPn4J5Pvroo27WrFl17sfuiyVOuKR6xv7qdMrxXHmu6D/6jw6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA7k6kCicSSfQ6N169Zu8ODB3sgh48i0adPqHHr44Ye71VZbrc72uA2jRo3yZo+4feFtG2+8sevXr194U52/b7nlFjd9+vQ629kAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJAlkGgckdPGkpxXOmGdX/zC7brbbq5jx45+17hx47xxJOPMCrbqrFVWWcU1b97cTs9GAFmyv1OnTq5v377uk08+cTfccINbtGhRbcSRJfstP8tA67169XI9evRw99xzj49yEr0/rf/4448+r7jzvXOoQP7sD5xF8PH1l/pT6zQMtz/aB+0Dfajt39AH9EF6aMnqA+2D9qFaYfWB+lFLwHjQPmgftA/0wfQAfUQfRcDqA/0D/QP9Q7Y9oI/oI/qYbQ/0D/QP9A/Z9kD/QP9A/5BtD/QP9A/0D9n2UPb+YYkPRN/Xve507do1GAcR8qDiBcvFixf7v/Xfyiuv7A4++GDXtm3bHDOI9uWLOKJ9+dJyyy3nDjvsMNeqVSt33XXXua+//jrfoTnb+/fv7zbYYAM3YsQIP7VNzk5WIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAASKJlBUxBE5S9q1a+cOOOCAnIzbt2/vWrZs6Y0jU6dOzb4xEhwv40nmDZLIerNmzdyBBx7ounTp4saMGeNmzJhR8PhwPscff7xr06aNGzp0qL+XQtcJn8dx+Z8HnArXV/jAB/1AP9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdKBadSDROJLjFImsKAKJzB+lRhzZYYcdXM+ePd0zzzzjHnrooUiu+VcVneS0005zM2fO9FPb6OZlXpk/f777/vvv85/IHghAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBOgQSjSOFHDMDBgzIGEeKjTiywgoruGOPPdYtWrTIDRs2zH333Xd5I5NEHVtrr722GzhwoHvjjTfcggUL3GabbeYUvURp3rx57umnn3bPPfecj15S6L6j+bKOM4z6gkMUHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0II06kGgcqWM1CW2oT8SRfv36uY033tg9+uij7sknnwzllvxn9+7dXe/evf2BMp7Mnj3bffPNN65Tp05ulVVW8dvff/99N3bsWG8eSc6RIyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEB6CSQaRwo5qkqNONK5c2c3aNAgP7XMFVdc4X766Sdv8CjWsdOnTx/XrVs3N2vWLHfbbbf5fOz+NtlkE7f33nu75s2bu4kTJ7rJkycXFcnk3HPPrdfTv/fee+t1HidBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEqonAyy+/XNT4vI3vs2xakV0SjSOFKmupEUd+//vfu65du/opZR5++OFCWcfua9u2rWvTpo378ssvvekkelCPHj1cr1693Ny5c93w4cOju2PXMY7EYmEjBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoCgCr7zySlHHcVDTJJBoHJHTx1ImMkiwQVst4si4cePctGnTsg6iJfvteJ3funVrd9JJJ/mIICOvvdZ9/MknmeO1X8mOrwn+Vv62rn1Ktp5vf7t27dwJJ5zgo5hcfPHFTtPZFLp/y6829+T8dbwZTS644ILM/ZRyvndOBSfUp3x2v/nKz/4lc67Bl/oV1AFrD7TPWgLGA/1Af+l/0AfTA/QRfRQBqw/0D/QP9A/Z9oA+oo/oY7Y90D/QP9A/ZNsD/QP9A/1Dtj3QP9A/0D9k2wP9A/0D/UO2PdA/0D/QP2TbQ8X1D4EPItE4YoWKW5YScURTzOy5557uiy++cCNGjIjLrsHb9MP3WWed5Qt19dVXu88//7zBeUYzMOPIhRdeGN3FOgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEKgoAonGkUJzC1nEkVtvvdVNnTo1+8ZiYOCInnfIIYe4Nddc002aNMk9/vjjdfZHj4+ut2zZ0u2yyy5+ipoHH3ww9nyLOKIncOmll7oFCxbEHpd5szLmPqPXja6bcSQccaQh+UXzZ71pzeXE8+B50L7r6jntgnZBu6BdoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOlA9OpBoHClkgyk24ogucuaZZ/pIIDfffLN7//33C2Wbd9/JJ5/s2rRp466//no3e/bsOsd1797d9e7d23311Vdu2LBhdfaXY4MZR4g4Ug6a5AEBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAwLIkkGgcKeQUKzbiSKdOndwRRxzhy/n3v//dzZ8/v2AkkJ49e7qOHTu6H374wd13332ZSCb77LOP23TTTd1nn33mxo4d6+bNm5fJZ/XVV3d//OMfXevWrd3dd9/tXn311cx55XQ6mXGEiCM46MpZrwq1M65TPU49njO6QXumPaMD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADjQ1HUg0jhRytRQbcWTjjTd2/fr180aQyy67rFCWft/AgQNd586dvTHk8ssvzxzfqlUrd/jhh7v27du7hQsXunfffdd9/fXXrkOHDm7dddf1EU3efPNNd+edd2bOKfcfZhwh4ki5yZIfBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJLm0CicaSQ06XYiCO9evVyO+ywg/voo4/cDTfckBgJZNCgQRnjiCKUhB1Xyy23nM+rW7durnnz5p6X7vHLL790jz/+eKNFGjEOZhwh4ghOwHC9tPrBknpBvcAhig6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA5WkA4nGkaXtZCn2errxlVZayU9NM2fOHPfjjz8We2qDjjPjCBFHGoSRkyEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQaAIEEo0jckJZyjhigg3aautp2m/GkXDEkTSV3zvjUvz8KX/gDOT5p1b/qP/Uf9p/ej//0P5p/7R/2n9av/+hf+gf+of+oX/p/P0P/Uf/0X/0H/1H/9M4/kX/R/9H/0f/R/+X4v6vpsYlGkfMFMGyloAZR4g4Qo2AAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKDSCSQaR7zDMHCYsAychgEHM46EI46Y85Ql9YR2UttO4AAH9BA9RAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAcqQwcSjSOV7owp9/2bcYSII+UmS34QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCxtAonGERxAuQ4gM44QcSSXC/UEHjhGcYyiA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOlB5OpBoHFnaTpamfj0zjhBxpKk/Ke4PAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEkAonGERxhuY4wM44QcSSXC/UEHjgHK885SLul3dJuabfoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAOJxpEk50na9ptxhIgjaXvylBcCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQfQQSjSNymFnKOI2CDdpq62nab8aRcMSRNJXfOw5T/Pwpf+C45PmnVv+o/9R/2n96P//Q/mn/tH/af1q//6F/6B/6h/6hf+n8/Q/9R//Rf/Qf/Uf/0zj+Rf9H/0f/R/9H/5fi/q+mxiUaR8wUwbKWgBlHiDhCjYAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoNIJJBpHvMMwcJiwDJyGAQczjoQjjpjzlCX1hHZS207gAAf0ED1EB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1ABypDBxKNI5XujCn3/ZtxhIgj5SZLfhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEILG0CicYRHEC5DiAzjhBxJJcL9QQeOEZxjKID6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6UHk6kGgcWdpOlqZ+PTOOEHGkqT8p7g8CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgSQCicYRHGG5jjAzjhBxJJcL9QQeOAcrzzlIu6Xd0m5pt+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA4nGkSTnSdr2m3GEiCNpe/KUFwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFB9BBKNI3KYWco4jYIN2mrradpvxpFwxJE0ld87DlP8/Cl/4Ljk+adW/6j/1H/af3o//9D+af+0f9p/Wr//oX/oH/qH/qF/6fz9D/1H/9F/9B/9R//TOP5F/0f/R/9H/0f/l+L+r6bGJRpHzBTBspaAGUeIOEKNgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECg0gkkGke8wzBwmLAMnIYBBzOOhCOOmPOUJfWEdlLbTuAAB/QQPUQH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAHKkMHEo0jle6MKff9m3GEiCPlJkt+EIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgsbQKJxhEcQLkOIDOOEHEklwv1BB44RnGMogPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADpQeTqQaBxZ2k6Wpn49M44QcaSpPynuDwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIACBJAKJxhEcYbmOMDOOEHEklwv1BB44ByvPOUi7pd3Sbmm36AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqADicaRJOdJ2vabcYSII2l78pQXAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUH0EEo0jcphZyjiNgg3aautp2m/GkXDEkTSV3zsOU/z8KX/guOT5p1b/qP/Uf9p/ej//0P5p/7R/2n9av/+hf+gf+of+oX/p/P0P/Uf/0X/0H/1H/9M4/kX/R/9H/0f/R/+X4v6vpsYlGkfMFMGyloAZR4g4Qo2AAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQKDSCSQaR7zDMHCYsAychgEHM46EI46Y85Ql9YR2UttO4AAH9BA9RAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAcqQwcSjSOV7owp9/2bcYSII+UmS34QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCxtAonGERxAuQ4gM44QcSSXC/UEHjhGcYyiA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOlB5OpBoHFnaTpamfj0zjhBxpKk/Ke4PAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEkAonGERxhuY4wM44QcSSXC/UEHjgHK885SLul3dJuabfoADqADqAD6AA6gA6gA+gAOoAOoAPoADqADqAD6AA6gA6gA+gAOoAOoAOJxpEk50na9ptxhIgjaXvylBcCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQfQQSjSNymFnKOI2CDdpq62nab8aRcMSRNJXfOw5T/Pwpf+C45PmnVv+o/9R/2n96P//Q/mn/tH/af1q//6F/6B/6h/6hf+n8/Q/9R//Rf/Qf/Uf/0zj+Rf9H/0f/R/9H/5fi/q+mxiUaR8wUwbKWgBlHiDhCjYAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoNIJJBpHvMMwcJiwDJyGAQczjoQjjpjzlCX1hHZS207gAAf0ED1EB9ABdAAdQAfQAXQAHUAH0AF0AB1AB9ABdAAdQAfQAXQAHUAH0AF0AB1ABypDBxKNI5XujCn3/ZtxhIgj5SZLfhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEILG0CicYRHEC5DiAzjhBxJJcL9QQeOEZxjKID6AA6gA6gA+gAOoAOoAPoQFgHNr3s+ZzfOF45ZUsfyZN6Qj0J1xPqA/WB+sDnB3QAHUAH0AF0AB1AB9ABdAAdQAeWvQ4kGkdyfuVhJTNVDRFHqAwQgAAEIAABCEAAAhCAAAQgAIH8BKLGkVdP3Sr/weyBAAQgAAEIQAACEIAABCAAAQhAAAIQWGYEEo0jOLxyHV5EHMnlQf2ABw7AZe8ApB3SDmmHtEN0AB1AB9ABdAAdaIo6EDWOEHGEetoU6yn6Sb2kXvI5Ch1AB9ABdAAdQAfQAXQAHUAH0IEal2gcWWaWliZ6YTOOEHGkiT4gbgsCEIAABCAAAQhAAAIQgAAEmgSBqHGEiCNN4rFwExCAAAQgAAEIQAACEIAABCAAAQhAoA6BROOIHFaWMk6bYIO22nqa9ptx5IILLkhl+b3jLsXPn/IHjkOef2r1j/pP/af9p/fzD+2f9k/7p/2n9fsf+tcw/YsaRxRxxJL9noC+oC/oSzp/X0NfG6av8IMf/Sf9J/0n/ad9nubzdS0B44E+oo/oI/poeoA+lqiPNUQcsTpT9NKMI0QcKRoZB0IAAhCAAAQgAAEIQAACEIBACglEjSNEHElhJaDIEIAABCAAAQhAAAIQgAAEIAABCFQEgaIijpgzh2WNM+NIOOIIXJj7zL/pETixWAZvfMCBehBEqqIeoAfoIe0AHUAH0AF0AB1AB6LGEUUcoV5QL+gf6B/QAXQAHUAH0AF0AB1AB9ABdAAdQAfQgaanA4nGkYqwvyzFmzTjCBFHliJ0LgUBCEAAAhCAAAQgAAEIQAACFUcgahwh4kjFPUJuGAIQgAAEIAABCEAAAhCAAAQgAIGUEEg0juD4ynV8mXGEiCO5XKgn8MAZ2PScgbRL2iXtknaJDqAD6AA6gA6gA8tSB6LGESKOUB+XZX1ED6l/1D8+F6ED6AA6gA6gA+gAOoAOoAPoADqQXwcSjSMpMdAUXUwzjhBxpGhkHAgBCEAAAhCAAAQgAAEIQAACKSQQNY4QcSSFlYAiQwACEIAABCAAAQhAAAIQgAAEIFARBBKNIzivcp1XZhwh4kguF+oJPHDo5Xfo0T5oH7QP2gc6gA6gA+gAOoAOpFEHosYRIo7QDtLYDtB/6j31ns+B6AA6gA6gA+gAOoAOoAPoADpQCTqQaBypCPvLUrxJM44QcWQpQudSEIAABCAAAQhAAAIQgAAEIFBxBKLGESKOVNwj5IYhAAEIQAACEIAABCAAAQhAAAIQSAmBROOIHFCWMk6YYIO22nqa9ptxJBxxJE3l9464FD9/yh84Ann+qdU/6j/1n/af3s8/tH/aP+2f9p/W73/oX8P0L2ocUcQRS/Z7AvqCvqAv6fx9DX1tmL7CD370n/Sf9J/0n/Z5ms/XtQSMB/qIPqKP6KPpAfpYoj7W1LhE44hBZVlLwIwjRByhRkAAAhCAAAQgAAEIQAACEIAABPITiBpHiDiSnxV7IAABCEAAAhCAAAQgAAEIQAACEIDAsiSQaBzxDvbAYcIycLIHHMw4Eo44Ys4lltQT2kltO4EDHNBD9BAdQAfQAXQAHUAH0AF0IGocUcQR6gX1gv6B/gEdQAfQAXQAHUAH0AF0AB1AB9ABdAAdaHo6kGgcWZaulqZ4bTOOEHGkKT4d7gkCEIAABCAAAQhAAAIQgAAEmgqBqHGEiCNN5clwHxCAAAQgAAEIQAACEIAABCAAAQhAIJdAonEEx1eu48uMI0QcyeVCPYEHzsCm5wykXdIuaZe0S3QAHUAH0AF0AB1YljoQNY4QcYT6uCzrI3pI/aP+8bkIHUAH0AF0AB1AB9ABdAAdQAfQgfw6kGgcyfWZsGbGESKOUBcgAAEIQAACEIAABCAAAQhAAAL5CUSNI0Qcyc+KPRCAAAQgAAEIQAACEIAABCAAAQhAYFkSSDSO4LzKdV6ZcYSII7lcqCfwwKGX36FH+6B90D5oH+gAOoAOoAPoADqQRh2IGkeIOEI7SGM7QP+p99R7PgeiA+gAOoAOoAPoADqADqAD6EAl6ECicWRZulqa4rXNOELEkab4dLgnCEAAAhCAAAQgAAEIQAACEGgqBKLGESKONJUnw31AAAIQgAAEIAABCEAAAhCAAAQgAIFcAonGETmgLGWcMMEGbbX1NO0340g44kiayu8dcSl+/pQ/cATy/FOrf9R/6j/tP72ff2j/tH/aP+0/rd//0L+G6V/UOKKII5bs9wT0BX1BX9L5+xr62jB9hR/86D/pP+k/6T/t8zSfr2sJGA/0EX1EH9FH0wP0sUR9rKlxicYRgxq3FHiDry8saUhmHCHiSBqeNmWEAAQgAAEIQAACEIAABCAAgfoSiBpHiDhSX5KcBwEIQAACEIAABCAAAQhAAAIQgAAEGpdAonHEO9gDg0jccvvtt3c777yzu/XWW93UqVMzJhIzk2i50koruT59+hQsxaOPPupmzZoVe37cdVdccUW34447ujXWWMN16NDBzZ8/33322Wfuueeec9OnTy86n/B9xl0nbr8ZR8IRR+KOKzY/jgveEMhTv9ge3+7gAhd0A91AB9ABdAAdQAfQAXQAHagEHYgaRxRxpBLum/ZF+6Ke0s+iA+gAOoAOoAPoADqADqAD6AA6gA6kTQcSjSP5HB+tW7d2gwcP9sYQGUemTZsWe+jGG2/s+vXrF7vPNt5yyy3e8GHrhZZrrbWW+8Mf/uB0fT2suXPnOhlJWrRo4U97/PHH3aRJkwpl0aB9Zhwh4kiDMHIyBCAAAQhAAAIQgAAEIAABCFQ5gahxhIgjVf7AKR4EIAABCEAAAhCAAAQgAAEIQAACFUsg0TgSddLohHXWWcftuuuurmPHjr7ghSKO9OrVy/Xo0cPdc889PipJND+t//jjj27RokWJbx7JHDJkyBAfZUSRRSZMmOC+++4716xZM7fVVlv5e9INjR492s2YMSMxv/o4xcw4QsQRnIb1qT9x9Z98cCxSL9ATdAAdQAfQAXQAHUAH0IFq1IGocYSII9Tzaqzn6Df1mnrN5zh0AB1AB9ABdAAdQAfQAXQAHagGHUg0jpglZuWVV3YHH3ywa9u2rWvevLlt9stCEUf69+/vNthgAzdixAj3xRdf5JxX6spGG23k9ttvP/f999+7f/7zn27hwoU5WfTt29dtuummbsqUKe6OO+7I2VeuFTOOEHGkXETJBwIQgAAEIAABCEAAAhCAAASqkUDUOELEkWp8ypQJAhCAAAQgAAEIQAACEIAABCAAgWogkGgcMYdUu3bt3AEHHJBT5vbt27uWLVu6QhFHjj/+eNemTRs3dOhQf67lV5/lPvvs440hzz//vHvggQfqRBTRNDaHHHKIj2By2WWXucWLF/vpbMrp8DHjCBFHcI6Vs17Vpz1wfRys1Bt0CB1AB9ABdAAdQAfQAXSgKetA1DhCxBHqa1Our+gp9ZP6yecqdAAdQAfQAXQAHUAH0AF0AB1Isw4kGkdynCKRFUUg6dKlizeOTJs2LbLXuVatWrnTTjvNzZw5091www1OF5PZZP78+T5qSJ0TEjYcfvjhbrXVVvNT1Lzxxht1jtaUNWeccYa/zvDhw93cuXPrHNPQDWYcIeJIQ0lyPgQgAAEIQAACEIAABCAAAQhUM4GocYSII9X8tCkbBCAAAQhAAAIQgAAEIAABCEAAApVMINE4ImeVpYzDJtigrQMGDPDGkXHjxjkZR6L71157bTdw4EAnk8eCBQvcZptt5mTuUJo3b557+umn3XPPPecjg2hb9Hxb1z6lU045xUcvGX3jje7DGTMyx9furT3/xBNPdCussIIbM2aM++CDD3zEkfB+7xQLNuj+o/nbek2B/WYcCUccKWf+Db0/zg+ccAWeH3zgQ/2ov/7Rfmg/tB/aT30/P6Ef6Af6gX6gH/X7/lnp+hk1jijiiKVivn9Xevm5f/o/+j/6P/q/dPZ/6D/6j/6j/+g/+m/fd/j+U0vAeKCP6CP62IT1sabGB+dQe1Wq6dq1a/C5Vo8sqLjBUlO9FEpJEUe6d+/uevfu7bNYtGiRmz17tvvmm29cp06d3CqrrOK3v//++27s2LE5Bo981zzzzDNd8+bN3bXXXus+/fTT2MOOOuoon/ftt9/u3nnnndhjGrLRjCNEHGkIRc6FAAQgAAEIQAACEIAABCAAgWonEDWOEHGk2p845YMABCAAAQhAAAIQgAAEIAABCECgUgkUFXEk4wQLHCbeMb1kaRFHbr31Vjd16tRMBA87vk+fPq5bt25u1qxZ7rbbbvNT1Nj5m2yyidt77729EWTixIlu8uTJdc63fGx5zjnneKfLNddc440jtj28PPLII13Hjh3dHXfc4aZMmZJzv+Hj7D7MCFLqA7z33ntLPYXjIQABCEAAAhCAAAQgAAEIQAACVUvgvweOLFi2mnGDC+5nJwQgAAEIQAACEIAABCAAAQhAAAKVS+Dll19OHO+PG6+3cXuWQeS2iB9jaa4nGkcKVc2kiCNt27b1U8t8+eWX7qeffqqTVY8ePVyvXr3c3Llz3fDhw+vsj26wiCMjR450n3zySXS3Xz/66KNdhw4d3Pjx493bb78de0x4I8aRMA3+hgAEIAABCEAAAhCAAAQgAAEI1I8AxpH6ceMsCEAAAhCAAAQgAAEIQAACEIBANRB45ZVXqqEYqS1DonGkkLMnKeJIkgOmXbt27oQTTvBRQS6++GKn6WwKXe/00093rVq1cjfccIP76KOPYh03xx9/vFO+t9xyi5s+fXrB/JLuL26/GU0uuOCC2OsXuv+4/Dh+2Tqn4A9/2mVuJCl4wANdRBfRAXQAHUAH0AF0oL46EJ2aJvpLyyunbMn36GX45lB9nyvn0S/QL9AvoAPoADqADqAD6AA6gA6gA+gAOlD9OpBoHIn+0BNeT4o4Ej427m9VsLPOOstPP3P11Ve7zz//PO6wzDabhmbChAnujTfeyGy3P5o1a+bOOOOMzHQ2n332me0q29KMIxdeeGHZ8iQjCEAAAhCAAAQgAAEIQAACEIBApRNIMo68eupWlV5E7h8CEIAABCAAAQhAAAIQgAAEIAABCFQlgUTjSCEHWaGIIy1btnS77LKLn6LmwQcfjI38YRFHRPbSSy91CxYsiD3OHEz9+/d3G264oXvyySfdI488UudNpVVXXdUNGTLELV682Oe3cOHCgvlZvqUszThCxBGcdaXUm0LtiHyq36HH80cvaOe0c3QAHUAH0AF0AB1Igw4kGUeIOEI7SEM7QO+p59RzPvehA+gAOoAOoAPoADqADqAD6EAl6kCicaSQXSYp4sjJJ5/s2rRp466//no3e/bsOll1797d9e7d23311Vdu2LBhdfZHN2y55ZZujz328MdfddVV3pQSPmb33Xd3W2+9tZsxY4YbPXp0eFfZ/jbjCBFHyoaUjCAAAQhAAAIQgAAEIAABCECgCggkGUeIOFIFD5kiQAACEIAABCAAAQhAAAIQgAAEIFCVBBKNI3JEWco4Y4IN2moRR8aNG+emTZuWjQCyZP8+++zjNt10U6cpY8aOHevmzZtXGwEk2L/6Gmu4P/7xj65169burrvucq+99lrm/J49e7qOHTu6H374wd13333B0bVphRVWcMccc4xTNJOnn37aPfroo27Rov9n795jLSvr+48/O4wMDrfhFgpRtJgKSA0ol6atAtZiFYjYysUYA0NboKU20JTWGvsP/FFTCZhYjdSqVUAujRiLpvYnKhYRK5OWIYLNDNcgt4LcFBBnYPpba53z3Zc1Z85z9mGfYZ39vHYCe6+9zl5n7ff6Pu/1zN6f9T0vNitf/epXpzVr1jR/puZr112X/vvWW/vbi9e39z+Wx1kfwZHhjiPjvL5JmFUvqPkt5vd7fZVQw0/9GD/8Mfu34atSaG7hU35wfnF+Nb8IH/ADP9YEoh6cH5wfnB8G42Ep/ZgLjtQdR+JmfM5egVcBUZ/bpj7n+3wr6lF9zhAIHs6fxic/8VP4gB/5sSYQ9eD84Pzg/DAYD/zIj/w4GA/OD8v8/FB975YNjoT05rrPdRxZuXJlOvPMM9Puu++e6j8bc9ddd6Wnn3467bHHHul1r3td88vvuOOOdO21145svg6A7Lfffk3Q5JJLLhlZd9hhh6Xjjz++ee65555L69evT/vss0+q/0xNfbvvvvvS5Zdf3gRUmicm/L8Ijug4MmGwNocAAggggAACCCCAAAIIILCsCeSCIzqOLOvDa+cRQAABBBBAAAEEEEAAAQQQQGCKCWSDI02Hi9kru/uJ0tnl6Dhy1VVXpQ0bNgwSp0M//8pXvjIdddRRqQ58rFixokFZb/PJJ59MN954Y7rtttu2eN0ZZ5zRD45cfPHFW6w/8MADU/1naVavXt0/NHUw5ZZbbknf/e53my4k8+13+32MsxzBkeGOI+O8fqn2y3b9rSx16G/m8QAP8AAP8AAP8AAP8AAPvJweyAVH6o4jL+f+GR/Gh/pznuQBHuABHuABHuABHuABHuABHuCBuT2QDY70kxkv8UH9i+qgR/2naX7605+mjRs3vsQtplSHUupuJs8++2zTyaQ+yEt9i+CIjiNLTdr2EUAAAQQQQAABBBBAAAEElhOBXHBEx5HldDTtKwIIIIAAAggggAACCCCAAAIIlEQgGxyRuBlN3ERwRMeRUS7qBA8JTQlNHuABHuABHuABHuABHijbA7ngiI4jZdcHPzj+5gnmCTzAAzzAAzzAAzzAAzzAAzzQXQ9kgyMlpWgW8l4jOKLjyEJo+RkEEEAAAQQQQAABBBBAAIFSCOSCIzqOlFIJ3icCCCCAAAIIIIAAAggggAACCCw3AtngiOTXaPIrgiM6joxyUSd4SAh2NyFofBqfxqfxyQM8wAM8wAM8sC08kAuO6DiiDrdFHfKdOlNn5j08wAM8wAM8wAM8wAM8wAM8ML4HssGR5ZaEWer9jeCIjiNLTdr2EUAAAQQQQAABBBBAAAEElhOBXHBEx5HldDTtKwIIIIAAAggggAACCCCAAAIIlEQgGxypE1lx6ydzqifqZ2O5pPURHBnuOFLS+28SegUff++/Sig6/sX6T/2rf+O/3PmP8W/8G//Gf6n//uO/8fyXC47UHUfiFp8n8Au/8EuZn6/x63h+rXnFjT9nr5ysgPAHf8R4MD5mCAQP8yt+4Ed+DB/wIz/WBKIenB+cH+Y9P/R6KRscCam4nyEQwREdR1QEAggggAACCCCAAAIIIIAAAgMCueCIjiMDVh4hgAACCCCAAAIIIIAAAggggAACXSKQDY40VwBUCRP31ZUAFYcIjgx3HOkntXBSJ9UVIOqBL/jSOOABHuABHuABHuABHijRA7ngSN1xpEQufMAH6t68gAd4gAd4gAd4gAd4gAd4gAd4oOseyAZHupRy6cK+RHBEx5EuHA37gAACCCCAAAIIIIAAAggg0BUCueCIjiNdOVL2AwEEEEAAAQQQQAABBBBAAAEEEBglkA2OdD35sq33L4IjOo5IBkoGSgZua//4fbzDO7zDAzzAAzzAAzzQZQ/kgiM6jqjfLtcvv6pP9WmexQM8wAM8wAM8wAM8wAM8ULIHssGR0ZyJpQiO6DiiFhBAAAEEEEAAAQQQQAABBBAYEMgFR3QcGbDyCAEEEEAAAQQQQAABBBBAAAEEEOgSgWxwRLJqNFkVwREdR0a5qBM8Sk7gqX/1r/4l0XmAB3iAB3iAB3igl3LBER1HjBPjxPnS+ZIHeIAHeIAHeIAHeIAHeIAHeKCbHsgGR7qUcunCvkRwRMeRLhwN+4AAAggggAACCCCAAAIIINAVArngiI4jXTlS9gMBBBBAAAEEEEAAAQQQQAABBBAYJZANjtSJn7j1E2DVE/WzsVzS+giODHccKen9Nwmwgo+/918l4Bz/Yv2n/tW/8V/u/Mf4N/6Nf+O/1H//8d94/ssFR+qOI3GLzxP4hV/4pczP1/h1PL/WvOLGn7NX6FZA+IM/YjwYHzMEgof5FT/wIz+GD/iRH2sCUQ/OD84P854fer2UDY6EVNzPEIjgiI4jKgIBBBBAAAEEEEAAAQQQQACBAYFccETHkQErjxBAAAEEEEAAAQQQQAABBBBAAIEuEcgGR5orAKqEifvqSoCKQwRHhjuO9JNaOKmT6goQ9cAXfGkc8AAP8AAP8AAP8AAPlOiBXHCk7jhSIhc+4AN1b17AAzzAAzzAAzzAAzzAAzzAAzzQdQ9kgyNdSrl0YV8iOKLjSBeOhn1AAAEEEEAAAQQQQAABBBDoCoFccETHka4cKfuBAAIIIIAAAggggAACCCCAAAIIjBLIBke6nnzZ1vsXwREdRyQDJQMlA7e1f/w+3uEd3uEBHuABHuABHuiyB3LBER1H1G+X65df1af6NM/iAR7gAR7gAR7gAR7gAR4o2QPZ4MhozsRSBEd0HFELCCCAAAIIIIAAAggggAACCAwI5IIjOo4MWHmEAAIIIIAAAggggAACCCCAAAIIdIlANjgiWTWarIrgiI4jo1zUCR4lJ/DUv/pX/5LoPMADPMADPMADPNBLueCIjiPGiXHifOl8yQM8wAM8wAM8wAM8wAM8wAM80E0PZIMjXUq5dGFfIjii40gXjoZ9QAABBBBAAAEEEEAAAQQQ6AqBXHBEx5GuHCn7gQACCCCAAAIIIIAAAggggAACCIwSyAZH6sRP3PoJsOqJ+tlYLml9BEeGO46U9P6bBFjBx9/7rxJwjn+x/lP/6t/4L3f+Y/wb/8a/8V/qv//4bzz/5YIjdceRuMXnCfzCL/xS5udr/DqeX2tecePP2St0KyD8wR8xHoyPGQLBw/yKH/iRH8MH/MiPNYGoB+cH54d5zw+9XsoGR0Iq7mcIRHBExxEVgQACCCCAAAIIIIAAAggggMCAQC44ouPIgJVHCCCAAAIIIIAAAggggAACCCCAQJcIZIMjzRUAVcLEfXUlQMUhgiPDHUf6SS2c1El1BYh64Au+NA54gAd4gAd4gAd4gAdK9EAuOFJ3HCmRCx/wgbo3L+ABHuABHuABHuABHuABHuABHui6B7LBkS6lXLqwLxEc0XGkC0fDPiCAAAIIIIAAAggggAACCHSFQC44ouNIV46U/UAAAQQQQAABBBBAAAEEEEAAAQRGCWSDI11Pvmzr/YvgiI4jkoGSgZKB29o/fh/v8A7v8AAP8AAP8AAPdNkDueCIjiPqt8v1y6/qU32aZ/EAD/AAD/AAD/AAD/AAD5TsgWxwZDRnYimCIzqOqAUEEEAAAQQQQAABBBBAAAEEBgRywREdRwasPEIAAQQQQAABBBBAAAEEEEAAAQS6RCAbHJGsGk1WRXBEx5FRLuoEj5ITeOpf/at/SXQe4AEe4AEe4AEe6KVccETHEePEOHG+dL7kAR7gAR7gAR7gAR7gAR7gAR7opgeywZEupVy6sC8RHNFxpAtHwz4ggAACCCCAAAIIIIAAAgh0hUAuOKLjSFeOlP1AAAEEEEAAAQQQQAABBBBAAAEERglkgyN14idu/QRY9UT9bCyXtD6CI8MdR0p6/00CrODj7/1XCTjHv1j/qX/1b/yXO/8x/o1/49/4L/Xff/w3nv9ywZG640jc4vMEfuEXfinz8zV+Hc+vNa+48efsFboVEP7gjxgPxscMgeBhfsUP/MiP4QN+5MeaQNSD84Pzw7znh14vZYMjIRX3MwQiOKLjiIpAAAEEEEAAAQQQQAABBBBAYEAgFxzRcWTAyiMEEEAAAQQQQAABBBBAAAEEEECgSwSywZHmCoAqYeK+uhKg4hDBkeGOI/2kFk7qpLoCRD3wBV8aBzzAAzzAAzzAAzzAAyV6IBccqTuOlMiFD/hA3ZsX8AAP8AAP8AAP8AAP8AAP8AAPdN0D2eBIl1IuXdiXCI7oONKFo2EfEEAAAQQQQAABBBBAAAEEukIgFxzRcaQrR8p+IIAAAggggAACCCCAAAIIIIAAAqMEssGRridftvX+RXBExxHJQMlAycBt7R+/j3d4h3d4gAd4gAd4gAe67IFccETHEfXb5frlV/WpPs2zeIAHeIAHeIAHeIAHeIAHSvZANjgymjOxFMERHUfUAgIIIIAAAggggAACCCCAAAIDArngiI4jA1YeIYAAAggggAACCCCAAAIIIIAAAl0ikA2OSFaNJqsiOKLjyCgXdYJHyQk89a/+1b8kOg/wAA/wAA/wAA/0Ui44ouOIcWKcOF86X/IAD/AAD/AAD/AAD/AAD/AAD3TTA9ngSJdSLl3YlwiO6DjShaNhHxBAAAEEEEAAAQQQQAABBLpCIBcc0XGkK0fKfiCAAAIIIIAAAggggAACCCCAAAKjBLLBkTrxE7d+Aqx6on42lktaH8GR4Y4jJb3/JgFW8PH3/qsEnONfrP/Uv/o3/sud/xj/xr/xb/yX+u8//hvPf7ngSN1xJG7xeQK/8Au/lPn5Gr+O59eaV9z4c/YK3QoIf/BHjAfjY4ZA8DC/4gd+5MfwAT/yY00g6sH5wflh3vNDr5eywZGQivsZAhEc0XFERSCAAAIIIIAAAggggAACCCAwIJALjug4MmDlEQIIIIAAAggggAACCCCAAAIIINAlAtngSHMFQJUwcV9dCVBxiODIcMeRflILJ3VSXQGiHviCL40DHuABHuABHuABHuCBEj2QC47UHUdK5MIHfKDuzQt4gAd4gAd4gAd4gAd4gAd4gAe67oFscKRLKZcu7EsER3Qc6cLRsA8IIIAAAggggAACCCCAAAJdIZALjug40pUjZT8QQAABBBBAAAEEEEAAAQQQQACBUQLZ4EjXky/bev8iOKLjiGSgZKBk4Lb2j9/HO7zDOzzAAzzAAzzAA132QC44ouOI+u1y/fKr+lSf5lk8wAM8wAM8wAM8wAM8wAMleyAbHBnNmViK4IiOI2oBAQQQQAABBBBAAAEEEEAAgQGBXHBEx5EBK48QQAABBBBAAAEEEEAAAQQQQACBLhHIBkckq0aTVREc0XFklIs6waPkBJ76V//qXxKdB3iAB3iAB3iAB3opFxzRccQ4MU6cL50vA8fafAAAQABJREFUeYAHeIAHeIAHeIAHeIAHeIAHuumBbHCkSymXLuxLBEd0HOnC0bAPCCCAAAIIIIAAAggggAACXSGQC47oONKVI2U/EEAAAQQQQAABBBBAAAEEEEAAgVEC2eBInfiJWz8BVj1RPxvLJa2P4Mhwx5GS3n+TACv4+Hv/VQLO8S/Wf+pf/Rv/5c5/jH/j3/g3/kv99x//jee/XHCk7jgSt/g8gV/4hV/K/HyNX8fza80rbvw5e4VuBYQ/+CPGg/ExQyB4mF/xAz/yY/iAH/mxJhD14Pzg/DDv+aHXS9ngSEjF/QyBCI7oOKIiEEAAAQQQQAABBBBAAAEEEBgQyAVHdBwZsPIIAQQQQAABBBBAAAEEEEAAAQQQ6BKBbHCkuQKgSpi4r64EqDhEcGS440g/qYWTOqmuAFEPfMGXxgEP8AAP8AAP8AAP8ECJHsgFR+qOIyVy4QM+UPfmBTzAAzzAAzzAAzzAAzzAAzzAA133QDY40qWUSxf2JYIjOo504WjYBwQQQAABBBBAAAEEEEAAga4QyAVHdBzpypGyHwgggAACCCCAAAIIIIAAAggggMAogWxwpOvJl229fxEc0XFEMlAyUDJwW/vH7+Md3uEdHuABHuABHuCBLnsgFxzRcUT9drl++VV9qk/zLB7gAR7gAR7gAR7gAR7ggZI9kA2OjOZMLEVwRMcRtYAAAggggAACCCCAAAIIIIDAgEAuOKLjyICVRwgggAACCCCAAAIIIIAAAggggECXCGSDI5JVo8mqCI7oODLKRZ3gUXICT/2rf/Uvic4DPMADPMADPMADvZQLjug4YpwYJ86Xzpc8wAM8wAM8wAM8wAM8wAM8wAPd9EA2ONKllEsX9iWCIzqOdOFo2AcEEEAAAQQQQAABBBBAAIGuEMgFR3Qc6cqRsh8IIIAAAggggAACCCCAAAIIIIDAKIFscKRO/MStnwCrnqifjeWS1kdwZLjjSEnvv0mAFXz8vf8qAef4F+s/9a/+jf9y5z/Gv/Fv/Bv/pf77j//G818uOFJ3HIlbfJ7AL/zCL2V+vsav4/m15hU3/py9QrcCwh/8EePB+JghEDzMr/iBH/kxfMCP/FgTiHpwfnB+mPf80OulbHAkpOJ+hkAER3QcUREIIIAAAggggAACCCCAAAIIDAjkgiM6jgxYeYQAAggggAACCCCAAAIIIIAAAgh0iUA2ONJcAVAlTNxXVwJUHCI4MtxxpJ/UwkmdVFeAqAe+4EvjgAd4gAd4gAd4gAd4oEQP5IIjdceRErnwAR+oe/MCHuABHuABHuABHuABHuABHuCBrnsgGxyZL+VSF3gUef1GS7hFcETHkRKOtveIAAIIIIAAAggggAACCCCwUAK54IiOIwsl6ecQQAABBBBAAAEEEEAAAQQQQACBbUsgGxyZL/nylre8Jb397W9PV111VdqwYUM/RBJhkrjfc889U/2z++yzT9p9993Tz3/+8/TII4+km266KT300ENbfV28fvh+9erV6YQTTpiX0ne+85304IMPjrXd+d7n8O+P4IiOI5KBw3Wx0Prxc+pG3UiU8gAP8AAP8AAP8AAPTKsHcsERHUf4j//4b1r9533xG7/xGw/wAA/wAA/wAA/wwHL3QDY4srWExg477JDOOuusVAc56uDInXfeOeePvvGNb2yCHq94xSvSCy+8kJ588snmNfVyDe/b3/52uvnmm+d87VxPHnzwwem9733vXKv6z33pS19Kd999d395kg8iOKLjyCSp2hYCCCCAAAIIIIAAAggggMByJ5ALjug4styPsP1HAAEEEEAAAQQQQAABBBBAAIFpJZANjrSTMfUL9t9//3Tsscemvfbaq+GytY4ju+66a/rgBz+YVqxYkb73ve81/23atKlZPuqoo9Jb3/rWtHnz5vTFL34x3X///QvqEPK2t72ted2//uu/Nl1O2vtXL2/cuDG9+OKLC9reXK+fLxEWwREdRyQH56uTcevKz6sn9SSJygM8wAM8wAM8wAM8sNw9kAuO6DjCczzHc8vdc/afx3iMx3iAB3iAB3iAB3iAB6bVA9ngSCRmdtttt3TaaaelHXfcsQl+xPP1/dY6jhx33HHp8MMPT+vXr0/XXHPN8Euax6eccko68MAD09q1a9M3vvGNLdbP9cT73ve+9PrXvz596lOfSo8//vhcP7Kkz0VwRMeRJcVs4wgggAACCCCAAAIIIIAAAsuMQC44ouPIMjugdhcBBBBAAAEEEEAAAQQQQAABBIohkA2O1ImZ+lb/SZpTTz11BMzuu++ett9++3TllVc2f6qmn7Cqfqp+1R/90R+lV73qVVtd/6Y3vSm9+93vTg888ED63Oc+N+gQMvv62F780nr53HPPTatWrUof/ehHm6dj/+qF+Ple9bj+/bFcr6tvsfxS1kdwZLjjyMzWJ7P9JqFUbXCp9t/2qyQkvupryAfG7wyBSfiRX/iFX52/zV8G80HnF+eXmoDz6+wVKBULfhjUw8zoGCxP0/kzFxypO47EzfgwPvz7wb8fpsl/dT3Hjd/4jd/4jd/M//37Z/DvHefHGQLmB+YH5gfmB52fH1Q5jGxwJKQ+133dgeS1r33tVjuOnHfeeWmXXXZJn/zkJ9MTTzyxxSaOOOKI9K53vSvdd9996bLLLttiffuJlStXpg996ENN0OTzn/98s/N1eOXZZ59Nv/jFL9o/viTLERzRcWRJ8NooAggggAACCCCAAAIIIIDAMiWQC47oOLJMD6zdRgABBBBAAAEEEEAAAQQQQACBqSeQDY40CagqYTLX/emnn94PjmzYsGFwRd3sz9cbr19X3+Z6ff2nag466KB0yy23NH+qpp+428rve81rXpPWrFmTbr/99vTLX/4yHXrooWm77bZrtv/MM8+km2++Of3whz9sftdcvy+3/YWsj+DIcMeRhbxuqfbHdquE2lbqxfNzj1tccOEN3uABHuABHuABHuABHlgKD+SCI3XHkaX4vepZPasr5zUe4AEe4AEe4AEe4AEe4AEe4AEeeGkeyAZHmlTGVv6X6ziylZc1T9eBkZNPPjm9+OKL6dJLL02PP/74fD/erDv88MPTcccd1zyuX/fQQw+ln/3sZ2nvvfdOe+65Z/P8vffem6644op+YCW70TF/IIIjOo6MCc6PI4AAAggggAACCCCAAAIITDWBXHBEx5GpPvzeHAIIIIAAAggggAACCCCAAAIILGMC2eDIfMmcXMeRrSW76j9R8853vrP5UzPXX399+sEPfjBnR5L260844YR02GGHpQcffDBdffXVzZ+oif379V//9fTud787rVixIv3bv/1bWrt27YKuZIogyLjH8Lrrrhv3JX4eAQQQQAABBBBAAAEEEEAAgakl8H/v/8y876135VnzrrcSAQQQQAABBBBAAAEEEEAAAQSWL4Fbb711Qd/Px/f77l9ah5BJ88sGR+YrzXE7juyyyy7p+OOPT7/2a7+WNm/enL75zW82f6Zmvt8xvG7HHXdMq1atSk8++WR64YUXhlc1j9/61remt73tbemJJ55In/zkJ7dYP9cTgiNzUfEcAggggAACCCCAAAIIIIAAAuMREBwZj5efRgABBBBAAAEEEEAAAQQQQGCaCKxbt26a3k5x7yUbHJkvqTJOx5E3velN6R3veEdauXJlevTRR9NXv/rV9Mgjjyyo00i788jWlnfdddd03nnnNdv8u7/7u+bP4My3/1vbznzPR9DkggsukJjq+VtZk64v2+tWss7xcDzmOx+oD/WhPswDeIAHeIAHeGDUA7k/VbPur47w72j/jp7o50A8zMM8POphPPDgRV7kAR7gAR7gAR7gAR5YrAeywZH5ojQL6Tiy3XbbpT/4gz9IBx10UNq0aVO64YYbmi4jdceRSd/qgfCRj3yk+RM4n/70p9Njjz026V+RIjhy4YUXTnzbNogAAggggAACCCCAAAIIIIDAciWQC47c9tdHLte3Zr8RQAABBBBAAAEEEEAAAQQQQACBqSaQDY7UiZS49RNK1RP1s9Fx5Morr0x33nnn4Mqh2fX1xk899dT0+te/Pj388MPp2muvTY8//njqza6P7VWLzS2W51pfdyr53d/93eZP1Hzz//2/5vfHz8fro+NIvfz3f//36Ze//GVzJUusj5+fa/v1zyxkfQRHhjuOTHL7TQKo2mDNN/bH9mcIBI+XcvzwrZKW6sv44hd+nb3S1fnF+aUm4Pw6m0CvWJh/DephZnQMls0f1IfxMRgPxscMgbnOH7ngSN1xJG5zvX6+zx/i571+6/zxq02FT00gxovzt/O38/dgPMzqwfiIzlcVEPWhPuJ8YXzMEAgezp/8wI/8GD7gR36sCUQ9TP35oZonZoMjMSjmus91HHnjG9+Yfv/3f7/5kzRf/OIXmyDHXNtZ6HPnn39+WrVqVfrsZz+bHnrooS1edvjhh6fjjjsuPfXUU+kTn/jEFusn8UQER3QcmQRN20AAAQQQQAABBBBAAAEEEJgWArngiI4j03KkvQ8EEEAAAQQQQAABBBBAAAEEEJg2AtngSNOhIZLIrfvoOHLVVVelDRs2DBI3sz937rnnptWrV6fLLrss3XvvvVus7yd0Wts95phj0l577ZWef/759LWvfa3/uve85z3pkEMOSY8++mi64oor0jPPPNP/27j77rtv+sAHPpB22GGH9NWvfjXddttt/ddt7fcs5vkIjgx3HFnMdubjanv+9pT68DfoeIAHeIAHeIAHeIAHeIAHlpsHcsGRuuOIulbXy62u7a/zMW/xFg/wAA/wAA/wAA/wAA/wQAkeyAZH5kvKzNdxZJdddknnnXde8/L6z9TMd7vvvvvS9ddf3/+RNWvWpP32268JhlxyySX95+s/V3PmmWem3XffPW3atCnddddd6emnn0577LFHet3rXte0T7njjjuaP4nTf9GEH0RwRMeRCYO1OQQQQAABBBBAAAEEEEAAgWVNIBcc0XFkWR9eO48AAggggAACCCCAAAIIIIAAAlNMIBscmS9BNV/HkQMOOCCdeuqpC0K3fv36dPXVV/evPDrjjDP6wZGLL764/3yd5HnlK1+ZjjrqqHTYYYelFStWNNuv9/HJJ59MN95445J1GgkOERzRcUSyrIRkWdS9e/Wu3iWqeYAHeIAHeIAHeIAHch7IBUd0HOERHuGRnEes5wme4Ake4AEe4AEe4AEe4AEeeHk8kA2OLCj58TL8UL3j9Z/Bqf80zU9/+tO0cePGbbIXERzRcWSb4PZLEEAAAQQQQAABBBBAAAEElgmBXHBEx5FlciDtJgIIIIAAAggggAACCCCAAAIIFEcgGxyR6BlN9ERwRMeRUS7qBA8JUAlQHuABHuABHuABHuABHijbA7ngiI4jZdcHPzj+5gnmCTzAAzzAAzzAAzzAAzzAAzzQXQ9kgyPFRWkybziCIzqOZEBZjQACCCCAAAIIIIAAAgggUBSBXHBEx5GiysGbRQABBBBAAAEEEEAAAQQQQACBZUQgGxypk19x6yeAqifqZ2O5pPURHBnuOFLS+2+SgAUff++/SkI6/sX6T/2rf+O/3PmP8W/8G//Gf6n//uO/8fyXC47UHUfiFp8n8Au/8EuZn6/x63h+rXnFjT9nr9CsgPAHf8R4MD5mCAQP8yt+4Ed+DB/wIz/WBKIenB+cH+Y9P/R6KRscCam4nyEQwREdR1QEAggggAACCCCAAAIIIIAAAgMCueCIjiMDVh4hgAACCCCAAAIIIIAAAggggAACXSKQDY40VwBUCRP31ZUAFYcIjgx3HOkntXBSJ9UVIOqBL/jSOOABHuABHuABHuABHijRA7ngSN1xpEQufMAH6t68gAd4gAd4gAd4gAd4gAd4gAd4oOseyAZHupRy6cK+RHBEx5EuHA37gAACCCCAAAIIIIAAAggg0BUCueCIjiNdOVL2AwEEEEAAAQQQQAABBBBAAAEEEBglkA2OdD35sq33L4IjOo5IBkoGSgZua//4fbzDO7zDAzzAAzzAAzzQZQ/kgiM6jqjfLtcvv6pP9WmexQM8wAM8wAM8wAM8wAM8ULIHssGR0ZyJpQiO6DiiFhBAAAEEEEAAAQQQQAABBBAYEMgFR3QcGbDyCAEEEEAAAQQQQAABBBBAAAEEEOgSgWxwRLJqNFkVwREdR0a5qBM8Sk7gqX/1r/4l0XmAB3iAB3iAB3igl3LBER1HjBPjxPnS+ZIHeIAHeIAHeIAHeIAHeIAHeKCbHsgGR7qUcunCvkRwRMeRLhwN+4AAAggggAACCCCAAAIIINAVArngiI4jXTlS9gMBBBBAAAEEEEAAAQQQQAABBBAYJZANjtSJn7j1E2DVE/WzsVzS+giODHccKen9Nwmwgo+/918l4Bz/Yv2n/tW/8V/u/Mf4N/6Nf+O/1H//8d94/ssFR+qOI3GLzxP4hV/4pczP1/h1PL/WvOLGn7NX6FZA+IM/YjwYHzMEgof5FT/wIz+GD/iRH2sCUQ/OD84P854fer2UDY6EVNzPEIjgiI4jKgIBBBBAAAEEEEAAAQQQQACBAYFccETHkQErjxBAAAEEEEAAAQQQQAABBBBAAIEuEcgGR5orAKqEifvqSoCKQwRHhjuO9JNaOKmT6goQ9cAXfGkc8AAP8AAP8AAP8AAPlOiBXHCk7jhSIhc+4AN1b17AAzzAAzzAAzzAAzzAAzzAAzzQdQ9kgyNdSrl0YV8iOKLjSBeOhn1AAAEEEEAAAQQQQAABBBDoCoFccETHka4cKfuBAAIIIIAAAggggAACCCCAAAIIjBLIBke6nnzZ1vsXwREdRyQDJQMlA7e1f/w+3uEd3uEBHuABHuABHuiyB3LBER1H1G+X65df1af6NM/iAR7gAR7gAR7gAR7gAR4o2QPZ4MhozsRSBEd0HFELCCCAAAIIIIAAAggggAACCAwI5IIjOo4MWHmEAAIIIIAAAggggAACCCCAAAIIdIlANjgiWTWarIrgiI4jo1zUCR4lJ/DUv/pX/5LoPMADPMADPMADPNBLueCIjiPGiXHifOl8yQM8wAM8wAM8wAM8wAM8wAM80E0PZIMjXUq5dGFfIjii40gXjoZ9QAABBBBAAAEEEEAAAQQQ6AqBXHBEx5GuHCn7gQACCCCAAAIIIIAAAggggAACCIwSyAZH6sRP3PoJsOqJ+tlYLml9BEeGO46U9P6bBFjBx9/7rxJwjn+x/lP/6t/4L3f+Y/wb/8a/8V/qv//4bzz/5YIjdceRuMXnCfzCL/xS5udr/DqeX2tecePP2St0KyD8wR8xHoyPGQLBw/yKH/iRH8MH/MiPNYGoB+cH54d5zw+9XsoGR0Iq7mcIRHBExxEVgQACCCCAAAIIIIAAAggggMCAQC44ouPIgJVHCCCAAAIIIIAAAggggAACCCCAQJcIZIMjzRUAVcLEfXUlQMUhgiPDHUf6SS2c1El1BYh64Au+NA54gAd4gAd4gAd4gAdK9EAuOFJ3HCmRCx/wgbo3L+ABHuABHuABHuABHuABHuABHui6B7LBkS6lXLqwLxEc0XGkC0fDPiCAAAIIIIAAAggggAACCHSFQC44ouNIV46U/UAAAQQQQAABBBBAAAEEEEAAAQRGCWSDI11Pvmzr/YvgiI4jkoGSgZKB29o/fh/v8A7v8AAP8AAP8AAPdNkDueCIjiPqt8v1y6/qU32aZ/EAD/AAD/AAD/AAD/AAD5TsgWxwZDRnYimCIzqOqAUEEEAAAQQQQAABBBBAAAEEBgRywREdRwasPEIAAQQQQAABBBBAAAEEEEAAAQS6RCAbHJGsGk1WRXBEx5FRLuoEj5ITeOpf/at/SXQe4AEe4AEe4AEe6KVccETHEePEOHG+dL7kAR7gAR7gAR7gAR7gAR7gAR7opgeywZEupVy6sC8RHNFxpAtHwz4ggAACCCCAAAIIIIAAAgh0hUAuOKLjSFeOlP1AAAEEEEAAAQQQQAABBBBAAAEERglkgyN14idu/QRY9UT9bCyXtD6CI8MdR0p6/00CrODj7/1XCTjHv1j/qX/1b/yXO/8x/o1/49/4L/Xff/w3nv9ywZG640jc4vMEfuEXfinz8zV+Hc+vNa+48efsFboVEP7gjxgPxscMgeBhfsUP/MiP4QN+5MeaQNSD84Pzw7znh14vZYMjIRX3MwQiOKLjiIpAAAEEEEAAAQQQQAABBBBAYEAgFxzRcWTAyiMEEEAAAQQQQAABBBBAAAEEEECgSwSywZHmCoAqYeK+uhKg4hDBkeGOI/2kFk7qpLoCRD3wBV8aBzzAAzzAAzzAAzzAAyV6IBccqTuOlMiFD/hA3ZsX8AAP8AAP8AAP8AAP8AAP8AAPdN0D2eBIl1IuXdiXCI7oONKFo2EfEEAAAQQQQAABBBBAAAEEukIgFxzRcaQrR8p+IIAAAggggAACCCCAAAIIIIAAAqMEBEdGeWSXBEeyiPwAAggggAACCCCAAAIIIIBAgQQERwo86N4yAggggAACCCCAAAIIIIAAAghMBYFscKTrLVO29f5FcMSfqtFSSkspLaW2tX/8Pt7hHd7hAR7gAR7gAR7osgdywRF/qkb9drl++VV9qk/zLB7gAR7gAR7gAR7gAR7ggZI9kA2OTEU8ZoJvIoIj/lTNBKHaFAIIIIAAAggggAACCCCAwLInkAuO+FM1y/4QewMIIIAAAggggAACCCCAAAIIIDClBLLBEcmq0WRVBEd0HBnlok7wKDmBp/7Vv/qXROcBHuABHuABHuCBXsoFR3QcMU6ME+dL50se4AEe4AEe4AEe4AEe4AEe4IFueiAbHJnSwMyi31YER3QcWTRCL0QAAQQQQAABBBBAAAEEEJhCArngiI4jU3jQvSUEEEAAAQQQQAABBBBAAAEEEJgKAtngiMTPaOIngiM6joxyUSd4SIhKiPIAD/AAD/AAD/AAD/BA2R7IBUd0HCm7PvjB8TdPME/gAR7gAR7gAR7gAR7gAR7gge56IBscmYp4zATfRARHdByZIFSbQgABBBBAAAEEEEAAAQQQWPYEcsERHUeW/SH2BhBAAAEEEEAAAQQQQAABBBBAYEoJZIMjdfIrbv0EUPVE/Wwsl7Q+giPDHUdKev9NErDg4+/9V0lIx79Y/6l/9W/8lzv/Mf6Nf+Pf+C/133/8N57/csGRuuNI3OLzBH7hF34p8/M1fh3PrzWvuPHn7BWaFRD+4I8YD8bHDIHgYX7FD/zIj+EDfuTHmkDUg/OD88O854deL2WDIyEV9zMEIjii44iKQAABBBBAAAEEEEAAAQQQQGBAIBcc0XFkwMojBBBAAAEEEEAAAQQQQAABBBBAoEsEssGR5gqAKmHivroSoOIQwZHhjiP9pBZO6qS6AkQ98AVfGgc8wAM8wAM8wAM8wAMleiAXHKk7jpTIhQ/4QN2bF/AAD/AAD/AAD/AAD/AAD/AAD3TdA9ngSJdSLl3YlwiO6DjShaNhHxBAAAEEEEAAAQQQQAABBLpCIBcc0XGkK0fKfiCAAAIIIIAAAggggAACCCCAAAKjBLLBka4nX7b1/kVwRMcRyUDJQMnAbe0fv493eId3eIAHeIAHeIAHuuyBXHBExxH12+X65Vf1qT7Ns3iAB3iAB3iAB3iAB3iAB0r2QDY4MpozsRTBER1H1AICCCCAAAIIIIAAAggggAACAwK54IiOIwNWHiGAAAIIIIAAAggggAACCCCAAAJdIpANjkhWjSarIjii48goF3WCR8kJPPWv/tW/JDoP8AAP8AAP8AAP9FIuOKLjiHFinDhfOl/yAA/wAA/wAA/wAA/wAA/wAA900wPZ4EiXUi5d2JcIjug40oWjYR8QQAABBBBAAAEEEEAAAQS6QiAXHNFxpCtHyn4ggAACCCCAAAIIIIAAAggggAACowSywZE68RO3fgKseqJ+NpZLWh/BkeGOIyW9/yYBVvDx9/6rBJzjX6z/1L/6N/7Lnf8Y/8a/8W/8l/rvP/4bz3+54EjdcSRu8XkCv/ALv5T5+Rq/jufXmlfc+HP2Ct0KCH/wR4wH42OGQPAwv+IHfuTH8AE/8mNNIOrB+cH5Yd7zQ6+XssGRkIr7GQIRHNFxREUggAACCCCAAAIIIIAAAgggMCCQC47oODJg5RECCCCAAAIIIIAAAggggAACCCDQJQLZ4EhzBUCVMHFfXQlQcYjgyHDHkX5SCyd1Ul0Boh74gi+NAx7gAR7gAR7gAR7ggRI9kAuO1B1HSuTCB3yg7s0LeIAHeIAHeIAHeIAHeIAHeIAHuu6BbHCkSymXLuxLBEd0HOnC0bAPCCCAAAIIIIAAAggggAACXSGQC47oONKVI2U/EEAAAQQQQAABBBBAAAEEEEAAgVEC2eBI15Mv23r/Ijii44hkoGSgZOC29o/fxzu8wzs8wAM8wAM8wANd9kAuOKLjiPrtcv3yq/pUn+ZZPMADPMADPMADPMADPMADJXsgGxwZzZlYiuCIjiNqAQEEEEAAAQQQQAABBBBAAIEBgVxwRMeRASuPEEAAAQQQQAABBBBAAAEEEEAAgS4RyAZHJKtGk1URHNFxZJSLOsGj5ASe+lf/6l8SnQd4gAd4gAd4gAd6KRcc0XHEODFOnC+dL3mAB3iAB3iAB3iAB3iAB3iAB7rpgWxwpEsply7sSwRHdBzpwtGwDwgggAACCCCAAAIIIIAAAl0hkAuO6DjSlSNlPxBAAAEEEEAAAQQQQAABBBBAAIFRAtngSJ34iVs/AVY9UT8byyWtj+DIcMeRkt5/kwAr+Ph7/1UCzvEv1n/qX/0b/+XOf4x/49/4N/5L/fcf/43nv1xwpO44Erf4PIFf+IVfyvx8jV/H82vNK278OXuFbgWEP/gjxoPxMUMgeJhf8QM/8mP4gB/5sSYQ9eD84Pww7/mh10vZ4EhIxf0MgQiO6DiiIhBAAAEEEEAAAQQQQAABBBAYEMgFR3QcGbDyCAEEEEAAAQQQQAABBBBAAAEEEOgSgWxwpLkCoEqYuK+uBKg4RHBkuONIP6mFkzqprgBRD3zBl8YBD/AAD/AAD/AAD/BAiR7IBUfqjiMlcuEDPlD35gU8wAM8wAM8wAM8wAM8wAM8wANd90A2ONKllEsX9iWCIzqOdOFo2AcEEEAAAQQQQAABBBBAAIGuEMgFR3Qc6cqRsh8IIIAAAggggAACCCCAAAIIIIDAKIFscGS+5Ev94rht3ry5iCuHIjii44hkoGSgZOB8flQf6kN9OE/wAA/wAA/wAA+U5oFccETHEV7kRV4szYveL+/xHu/xAA/wAA/wAA/wAA8sFw9kgyMRDJnr/rd/+7fT29/+9nTVVVelO++8c64faZ7beeed0zHHHJP23XfftMcee6Rnn302Pfroo+mHP/xhuueee7b6uq2tmPT2tvZ75no+giM6jsxFx3MIIIAAAggggAACCCCAAAKlEsgFR3QcKbUyvG8EEEAAAQQQQAABBBBAAAEEEOg6gWxwZGsJmJUrV6azzz47rV69ugmObNiwYc6OI6961avS+9///rTDDjukeltPPPFE2mWXXdIrXvGKhs2NN96Yvvvd7zbrFpK4mvT2tvb+tvZ8BEd0HJEQXEi9bq2OPK9+1I+EKQ/wAA/wAA/wAA/wwLR5IBcc0XGE93iP96bNe94Pr/Ear/EAD/AAD/AAD/AAD0yLB7LBkXbypX7B/vvvn4499ti01157Nau31nFkxYoVTbik7jJy9913p6985SvpF7/4Raq3ceSRRzbbqDfwhS98If3kJz9p/6otlie9vS1+wQKeiOCIjiMLgOVHEEAAAQQQQAABBBBAAAEEiiGQC47oOFJMKXijCCCAAAIIIIAAAggggAACCCCwzAhkgyN1Qqa+7bbbbun0009PO+64Y6oDHMO3K6+8svlTNf1EVbWyftXBBx+cTjrppCYs8vGPfzy98MILM51FZte/5z3vSYccckj6n//5n/Qv//Ivg44ls+tje9Vic9va9mL91rYX62N7veqJev9ieZz1ERwZ7jgyzuubxNFL+P1eXyUX8Vt0/aof9WP8LN7/xo/xY/wYP4udP/IHf/AHf5Tij1xwpO44Erf497jxYXyUMj7q+UDc1P/sFYkVEMd/cZ9Pml+aXzp/8gd/8mfMJ8wvZggED37kR37kx/ABP47px16vaf5R86tvvYMOOqj6d8fMP2Tr+82bN89ssfp//SdpTjnllP5y/WD33XdP22+/ffOnau68886RdfXCiSee2ARDbrnllvTv//7vW6yv/+zMH/7hH6aNGzemj33sYyO/b4sfXoLtzfU7cs9FcETHkRwp6xFAAAEEEEAAAQQQQAABBEoikAuO6DhSUjV4rwgggAACCCCAAAIIIIAAAgggsJwILKjjSCRz2vd1B5LXvva1TXBkw4YN/Q4e8XNnnXVW2meffZo/UfOjH/1oi/V155IPf/jDTXrlH/7hH9KTTz4505GkSrI0yfnW/aS3F/s5zn0ER4Y7jozz+rnel9fPfbxxwcV4qa6gaXnQsnFhXBgXPMADPMADPMADXfVALjhSdxxRv+q3q/Vrv5xf+YmfeIAHeIAHeIAHeIAHeIAHSvZANjgyXwrmtNNOS6+dDY7M1XHk/PPPT6tWrUpf+MIX0v333z/npv7iL/4i7bzzzunyyy9P995775w/E09Oenux3XHuIzii48g41PwsAggggAACCCCAAAIIIIDAtBPIBUd0HJn2CvD+EEAAAQQQQAABBBBAAAEEEEBguRLIBkfmS1blOo585CMfSXVXkX/8x39MjzzyyJxXFp1zzjlpzz33TFdffXWqu5bM9/smvb3FJIYiOKLjiMTZYupnvvq2PUlW9cErPMADPMADPMADPMADy9kDueCIjiPqeznXNz+rX/VrnsYDPMADPMADPMADPMADPDDNHsgGR+ZLxOQ6jvzt3/5t82doLr300vToo4/Ouak//dM/TXvttVf68pe/nH784x/P+TPx5KS3V283giDxOxZ6f9111y30R/0cAggggAACCCCAAAIIIIAAAlNP4P/e/5l532PvyrPmXW8lAggggAACCCCwWALteYh5x2JJeh0CCCCAAAKLJ7Bu3brFv9grX3YC2eDIfMmphXYc+cxnPpMefvjhOTuO/Nmf/VnaY4890jXXXJPWr1+/oI4jk9penQgSHHnZa9AOIIAAAggggAACCCCAAAIITAGB9hc27bfkC5w2EcsIIIAAAgggMCkC7XmIecekyNoOAggggAACCydw6623zpkHmC9vMM0dPJbb+84GR+YrhVzHkQ996ENp5cqV6fOf/3x64IEH5tzUueeem3bdddd05ZVXprvuumvOn4knJ7292O449xE0ufDCC8d5mZ9FAAEEEEAAAQQQQAABBBBAYKoJ5P5UzW1/feRUv39vDgEEEEAAAQRePgLteYh5x8t3LPxmBBBAAAEEEFieBLLBkToJE7d+4qd6on42Oo7UoY8777xzkCCaXX/OOec0f4bmK1/5SvrRj360xfoVK1akD3/4w82fs/n0pz+dHnvssZmOI7Ovj99XLTa3rW0v1m9te7E+tternqj3P5bHWR/BkQsuuGBRr2+SRS/h93t99bez8Ft0/aof9WP8LN7/xo/xY/wYP4udP/IHf/AHf5Tij/YXNvFv7bhf91dHxMP+v6eND+OjlPFRzwfiFp9HqX/1r/4X9/ms+bX59Vz+PPSitaHZ5r6ed4RvY0Usz/X6+mes7837/Qw++PAv//Kn+av562C+MHXzi+ovtWSDI/Gm57rPdRw59dRT0wEHHJBuuumm9J3vfGeLTey9997p7LPPTps3b04f+9jH0saNG7f4meEnJr294W0v9HEER3QcWSgxP4cAAggggAACCCCAAAIIIFACgVxwxJW/JVSB94gAAggggMDLQ6A9DzHveHmOg9+KAAIIIIAAAsuXQDY40iQIq4TJXPfRceSqq65KGzZsGCRyZ3/+yCOPTO9617vSU089lT71qU+lF198cWQ773znO9Nv/MZvpPvvvz/98z//8xav7ydYl2h77e0vZDmCI8MdRxbyurn4ed3cdYULLsZLlVzeinc9b3wYH8YHD/AAD/AAD/BAVz3Q/sKm/VHJ8JW/6lgdd7WO7ZfzLD/xEw8sTw+05yHmHcvzOBp/jpvzsPMwD/AAD7x8HsgGR9of9Awv5zqO7LTTTumDH/xg2n777dPNN9/cdB2pu4vUt1e/+tXNn7qpd+BrX/tauvXWW/ubPvroo5s/cfP888+nr3/96/3nF7u9/gYm8CCCIzqOTACmTSCAAAIIIIAAAggggAACCEwNgfYXNu035srfNhHLCCCAAAIIIDApAu15iHnHpMjaDgIIIIAAAgiUQiAbHJkv2ZXrOFIngt785jenE044oeH53HPPpfXr16d99tkn/cqv/Erz3H333Zcuv/zy5s/VRILojDPOSPvtt1965pln0sUXXzxy5f1ithfbncR9BEd0HJF4m0Q9zTe+bP/lS9Q5Lsa38Wf88QAP8AAP8AAP8MD4Hmh/YdP+YMWVv8aVcTX+uHI+Mm6MG+OGBxbmgfY8xLxjYdzUF07OM84zPMADPMAD4YFscKT9Qc/wcq7jSPzsAQcckH7v934vrV69Op5KmzZtSrfcckv6j//4j/TCCy/0n68frFmzph8cueSSS0bW1Qvjbm+LDbyEJyI4ouPIS4DopQgggAACCCCAAAIIIIAAAlNHoP2FTfsNuvK3TcQyAggggAACCEyKQHseYt4xKbK2gwACCCCAAAKlEMgGRyJhMon7VatWpd122y3VnUeeeuqphvFL2e6kt7eQRFUER3QckcBbSL28lPq2fQk/9cMzPMADPMADPMADPMADy8kD7S9s2h+suPJXPS+neuZf9apezcN4YHl5oD0PMe9YXsfPeHO8nHedd3mAB3jg5fdANjjS/qCn9OUIjug4UnoleP8IIIAAAggggAACCCCAAALDBNpf2Ayvqx+78rdNxDICCCCAAAIITIpAex5i3jEpsraDAAIIIIAAAqUQyAZH6oRX3PpJn+qJ+tlYLml9BEeGO46U9P6bxF/Bx9/7rxKPjn+x/lP/6t/4L3f+Y/wb/8a/8V/qv//4bzz/tb+wiX8rx3195W/c4vMEfuEXfinz8zV+Hc+vNa+48efslZgVEP7gjxgP9fg49KK1MUya++GOI7Eift78w/jhD/4IH/DDDIHgwY/8yI8F+7HXS9ngSEjT/QyBCI7oOKIiEEAAAQQQQAABBBBAAAEEEBgQyAVHXPk7YOURAggggAACCEyWQHseYt4xWb62hgACCCCAAALTTyAbHGmuAKgSJu6rKwEqDhEcGe440k/i4aROqitA1ANf8KVxwAM8wAM8wAM8wAM8UKIH2l/YtD9SGb7yt0Q+vMAL6t78gAd4gAeWzgPteYh5h/FmvC3deHM+M76ML+OLB6bTA9ngSPuDntKXIzii40jpleD9I4AAAggggAACCCCAAAIIDBNof2EzvK5+7MrfNhHLCCCAAAIIIDApAu15iHnHpMjaDgIIIIAAAgiUQiAbHJEYGk0MRXBEx5FRLuoEDwlTCVMe4AEe4AEe4AEe4AEeKNsD7S9s2h+suPK37PrgB8ffPME8gQd4YCk90J6HmHeot6WsNz5TX+rLvIYHeGAaPZANjrQ/6Cl9OYIjOo6UXgnePwIIIIAAAggggAACCCCAwDCB9hc2w+vqx678bROxjAACCCCAAAKTItCeh5h3TIqs7SCAAAIIIIBAKQSywRGJqdHEVARHdBwZ5aJO8JjGZJ26VtfqWnKcB3iAB3iAB3iABxbugfYXNu0PVlz5azwZTwsfT84/xovxYrzwwHgeaM9DzDvG46fe8HLecd7hAR7gAR7IBkfaH/SUvhzBER1HSq8E7x8BBBBAAAEEEEAAAQQQQGCYQPsLm+F19WNX/raJWEYAAQQQQACBSRFoz0PMOyZF1nYQQAABBBBAoBQC2eBInTCLWz9pVD1RPxvLJa2P4Mhwx5GS3n+TOCz4+Hv/VeLS8S/Wf+pf/Rv/5c5/jH/j3/g3/kv99x//jee/9hc28W/luK+v/I1bfJ7AL/zCL2V+vsav4/m15hU3/py9ErQCwh/8EeOhHh+HXrQ2hklzP9xxJFbEz5t/GD/8wR/hA36YIRA8+JEf+bFgP/Z6KRscCWm6nyEQwREdR1QEAggggAACCCCAAAIIIIAAAgMCueCIK38HrDxCAAEEEEAAgckSaM9DzDsmy9fWEEAAAQQQQGD6CWSDI80VAFXCxH11JUDFIYIjwx1H+kk8nNRJdQWIeuALvjQOeIAHeIAHeIAHeIAHSvRA+wub9kcqw1f+lsiHF3hB3Zsf8AAP8MDSeaA9DzHvMN6Mt6Ubb85nxpfxZXzxwHR6IBscaX/QU/pyBEd0HCm9Erx/BBBAAAEEEEAAAQQQQACBYQLtL2yG19WPXfnbJmIZAQQQQAABBCZFoD0PMe+YFFnbQQABBBBAAIFSCGSDIxJDo4mhCI7oODLKRZ3gIWEqYcoDPMADPMADPMADPMADZXug/YVN+4MVV/6WXR/84PibJ5gn8AAPLKUH2vMQ8w71tpT1xmfqS32Z1/AAD0yjB7LBkfYHPaUvR3BEx5HSK8H7RwABBBBAAAEEEEAAAQQQGCbQ/sJmeF392JW/bSKWEUAAAQQQQGBSBNrzEPOOSZG1HQQQQAABBBAohUA2OCIxNZqYiuCIjiOjXNQJHtOYrFPX6lpdS47zAA/wAA/wAA/wwMI90P7Cpv3Biit/jSfjaeHjyfnHeDFejBceGM8D7XmIecd4/NQbXs47zjs8wAM8wAPZ4Ej7g57SlyM4ouNI6ZXg/SOAAAIIIIAAAggggAACCAwTaH9hM7yufuzK3zYRywgggAACCCAwKQLteYh5x6TI2g4CCCCAAAIIlEIgGxypE2Zx6yeNqifqZ2O5pPURHBnuOFLS+28ShwUff++/Slw6/sX6T/2rf+O/3PmP8W/8G//Gf6n//uO/8fzX/sIm/q0c9/WVv3GLzxP4hV/4pczP1/h1PL/WvOLGn7NXglZA+IM/YjzU4+PQi9bGMGnuhzuOxIr4efMP44c/+CN8wA8zBIIHP/IjPxbsx14vZYMjIU33MwQiOKLjiIpAAAEEEEAAAQQQQAABBBBAYEAgFxxx5e+AlUcIIIAAAgggMFkC7XmIecdk+doaAggggAACCEw/gWxwpLkCoEqYuK+uBKg4RHBkuONIP4mHkzqprgBRD3zBl8YBD/AAD/AAD/AAD/BAiR5of2HT/khl+MrfEvnwAi+oe/MDHuABHlg6D7TnIeYdxpvxtnTjzfnM+DK+jC8emE4PZIMj7Q96Sl+O4IiOI6VXgvePAAIIIIAAAggggAACCCAwTKD9hc3wuvqxK3/bRCwjgAACCCCAwKQItOch5h2TIms7CCCAAAIIIFAKgWxwRGJoNDEUwREdR0a5qBM8JEwlTHmAB3iAB3iAB3iAB3igbA+0v7Bpf7Diyt+y64MfHH/zBPMEHuCBpfRAex5i3qHelrLe+Ex9qS/zGh7ggWn0QDY40v6gp/TlCI7oOFJ6JXj/CCCAAAIIIIAAAggggAACwwTaX9gMr6sfu/K3TcQyAggggAACCEyKQHseYt4xKbK2gwACCCCAAAKlEMgGRySmRhNTERzRcWSUizrBYxqTdepaXatryXEe4AEe4AEe4AEeWLgH2l/YtD9YceWv8WQ8LXw8Of8YL8aL8cID43mgPQ8x7xiPn3rDy3nHeYcHeIAHeCAbHGl/0FP6cgRHdBwpvRK8fwQQQAABBBBAAAEEEEAAgWEC7S9shtfVj1352yZiGQEEEEAAAQQmRaA9DzHvmBRZ20EAAQQQQACBUghkgyN1wixu/aRR9UT9bCyXtD6CI8MdR0p6/03isODj7/1XiUvHv1j/qX/1b/yXO/8x/o1/49/4L/Xff/w3nv/aX9jEv5Xjvr7yN27xeQK/8Au/lPn5Gr+O59eaV9z4c/ZK0AoIf/BHjId6fBx60doYJs39cMeRWBE/b/5h/PAHf4QP+GGGQPDgR37kx4L92OulbHAkpOl+hkAER3QcUREIIIAAAggggAACCCCAAAIIDAjkgiOu/B2w8ggBBBBAAAEEJkugPQ8x75gsX1tDAAEEEEAAgeknkA2ONFcAVAkT99WVABWHCI4MdxzpJ/FwUifVFSDqgS/40jjgAR7gAR7gAR7gAR4o0QPtL2zaH6kMX/lbIh9e4AV1b37AAzzAA0vngfY8xLzDeDPelm68OZ8ZX8aX8cUD0+mBbHCk/UFP6csRHNFxpPRK8P4RQAABBBBAAAEEEEAAAQSGCbS/sBleVz925W+biGUEEEAAAQQQmBSB9jzEvGNSZG0HAQQQQAABBEohkA2OSAyNJoYiOKLjyCgXdYKHhKmEKQ/wAA/wAA/wAA/wAA+U7YH2FzbtD1Zc+Vt2ffCD42+eYJ7AAzywlB5oz0PMO9TbUtYbn6kv9WVewwM8MI0eyAZH2h/0lL4cwREdR0qvBO8fAQQQQAABBBBAAAEEEEBgmED7C5vhdfVjV/62iVhGAAEEEEAAgUkRaM9DzDsmRdZ2EEAAAQQQQKAUAtngiMTUaGIqgiM6joxyUSd4TGOyTl2ra3UtOc4DPMADPMADPMADC/dA+wub9gcrrvw1noynhY8n5x/jxXgxXnhgPA+05yHmHePxU294Oe847/AAD/AAD2SDI+0PekpfjuCIjiOlV4L3jwACCCCAAAIIIIAAAgggMEyg/YXN8Lr6sSt/20QsI4AAAggggMCkCLTnIeYdkyJrOwgggAACCCBQCoFscKROmMWtnzSqnqifjeWS1kdwZLjjSEnvv0kcFnz8vf8qcen4F+s/9a/+jf9y5z/Gv/Fv/Bv/pf77j//G81/7C5v4t3Lc11f+xi0+T+AXfuGXMj9f49fx/Frziht/zl4JWgHhD/6I8VCPj0MvWhvDpLkf7jgSK+LnzT+MH/7gj/ABP8wQCB78yI/8WLAfe72UDY6ENN3PEIjgiI4jKgIBBBBAAAEEEEAAAQQQQACBAYFccMSVvwNWHiGAAAIIIIDAZAm05yHmHZPla2sIIIAAAgggMP0EssGR5gqAKmHivroSoOIQwZHhjiP9JB5O6qS6AkQ98AVfGgc8wAM8wAM8wAM8wAMleqD9hU37I5XhK39L5MMLvKDuzQ94gAd4YOk80J6HmHcYb8bb0o035zPjy/gyvnhgOj2QDY60P+gpfTmCIzqOlF4J3j8CCCCAAAIIIIAAAggggMAwgfYXNsPr6seu/G0TsYwAAggggAACkyLQnoeYd0yKrO0ggAACCCCAQCkEssERiaHRxFAER3QcGeWiTvCQMJUw5QEe4AEe4AEe4AEe4IGyPdD+wqb9wYorf8uuD35w/M0TzBN4gAeW0gPteYh5h3pbynrjM/WlvsxreIAHptED2eBI+4Oe0pcjOKLjSOmV4P0jgAACCCCAAAIIIIAAAggME2h/YTO8rn7syt82EcsIIIAAAgggMCkC7XmIecekyNoOAggggAACCJRCIBsckZgaTUxFcETHkVEu6gSPaUzWqWt1ra4lx3mAB3iAB3iAB3hg4R5of2HT/mDFlb/Gk/G08PHk/GO8GC/GCw+M54H2PMS8Yzx+6g0v5x3nHR7gAR7ggWxwpP1BT+nLERzRcaT0SvD+EUAAAQQQQAABBBBAAAEEhgm0v7AZXlc/duVvm4hlBBBAAAEEEJgUgfY8xLxjUmRtBwEEEEAAAQRKIZANjtQJs7j1k0bVE/WzsVzS+giODHccKen9N4nDgo+/918lLh3/Yv2n/tW/8V/u/Mf4N/6Nf+O/1H//8d94/mt/YRP/Vo77+srfuMXnCfzCL/xS5udr/DqeX2tecePP2StBKyD8wR8xHurxcehFa2OYNPfDHUdiRfy8+Yfxwx/8ET7ghxkCwYMf+ZEfC/Zjr5eywZGQpvsZAhEc0XFERSCAAAIIIIAAAggggAACCCAwIJALjrjyd8DKIwQQQAABBBCYLIH2PMS8Y7J8bQ0BBBBAAAEEpp9ANjjSXAFQJUzcV1cCVBwiODLccaSfxMNJnVRXgKgHvuBL44AHeIAHeIAHeIAHeKBED7S/sGl/pDJ85W+JfHiBF9S9+QEP8AAPLJ0H2vMQ8w7jzXhbuvHmfGZ8GV/GFw9MpweywZH2Bz2lL0dwRMeR0ivB+0cAAQQQQAABBBBAAAEEEBgm0P7CZnhd/diVv20ilhFAAAEEEEBgUgTa8xDzjkmRtR0EEEAAAQQQKIVANjgiMTSaGIrgiI4jo1zUCR4SphKmPMADPMADPMADPMADPFC2B9pf2LQ/WHHlb9n1wQ+Ov3mCeQIP8MBSeqA9DzHvUG9LWW98pr7Ul3kND/DANHogGxxpf9BT+nIER3QcKb0SvH8EEEAAAQQQQAABBBBAAIFhAu0vbIbX1Y9d+dsmYhkBBBBAAAEEJkWgPQ8x75gUWdtBAAEEEEAAgVIIZIMjElOjiakIjug4MspFneAxjck6da2u1bXkOA/wAA/wAA/wAA8s3APtL2zaH6y48td4Mp4WPp6cf4wX48V44YHxPNCeh5h3jMdPveHlvOO8wwM8wAM8kA2OtD/oKX05giM6jpReCd4/AggggAACCCCAAAIIIIDAMIH2FzbD6+rHrvxtE7GMAAIIIIAAApMi0J6HmHdMiqztIIAAAggggEApBLLBkTphFrd+0qh6on42lktaH8GR4Y4jJb3/JnFY8PH3/qvEpeNfrP/Uv/o3/sud/xj/xr/xb/yX+u8//hvPf+0vbOLfynFfX/kbt/g8gV/4hV/K/HyNX8fza80rbvw5eyVoBYQ/+CPGQz0+Dr1obQyT5n6440isiJ83/zB++IM/wgf8MEMgePAjP/JjwX7s9VI2OBLSdD9DIIIjOo6oCAQQQAABBBBAAAEEEEAAAQQGBHLBEVf+Dlh5hAACCCCAAAKTJdCeh5h3TJavrSGAAAIIIIDA9BPIBkeaKwCqhIn76kqAikMER4Y7jvSTeDipk+oKEPXAF3xpHPAAD/AAD/AAD/AAD5TogfYXNu2PVIav/C2RDy/wgro3P+ABHuCBpfNAex5i3mG8GW9LN96cz4wv48v44oHp9EA2ONL+oKf05QiO6DhSeiV4/wgggAACCCCAAAIIIIAAAsME2l/YDK+rH7vyt03EMgIIIIAAAghMikB7HmLeMSmytoMAAggggAACpRDIBkckhkYTQxEc0XFklIs6wUPCVMKUB3iAB3iAB3iAB3iAB8r2QPsLm/YHK678Lbs++MHxN08wT+ABHlhKD7TnIeYd6m0p643P1Jf6Mq/hAR6YRg9kgyPtD3pKX47giI4jpVeC948AAggggAACCCCAAAIIIDBMoP2FzfC6+rErf9tELCOAAAIIIIDApAi05yHmHZMiazsIIIAAAgggUAqBbHBEYmo0MRXBER1HRrmoEzymMVmnrtW1upYc5wEe4AEe4AEe4IGFe6D9hU37gxVX/hpPxtPCx5Pzj/FivBgvPDCeB9rzEPOO8fipN7ycd5x3eIAHeIAHssGR9gc9C11+85vfnN7whjcs6MdvvfXWdMcddyzoZ0855ZS0/fbbb/Vnb7/99rRu3bqtrn+pKyI4ouPISyXp9QgggAACCCCAAAIIIIAAAtNEoP2FTfu9ufK3TcQyAggggAACCEyKQHseYt4xKbK2gwACCCCAAAKlEMgGR+qEWdz6SaPqifrZWJ5r/bHveEf6zd/8zVg17/23vvWt9P3vf7+/vV7103Ntf6eddkp/+Zd/Oe+2brrppvSdb397ztfH/m5t+wtZH8GR4Y4jsUMLeX2T2NvK+/P62SQXPktWv+qvSoyqL/VV1UD4tnrY3GJZfcx9/sXH+cn5wxn41QUAAAfjSURBVPmDH/lxrn+fOT84P7TPD+0vbGKuFff1lb9xUz/qp10/9XLc1If6UB/mn+af5p/jzj8PvWhtnEaa++GOI7HC+cX5xfnF+cX5xfll3PNLfQ5x/nD+KOL80eulbHAkJlXj3u+8885pxx133OrL6q4hJ598ctpuu+3SZz/72fTEE09s9Wdjxf77758+8IEPpLpDSR02meu2adOm9MILL8y1aiLPRXBEx5GJ4LQRBBBAAAEEEEAAAQQQQACBKSGQC4648ndKDrS3gQACCCCAQAcJtOch5h0dPEh2CQEEEEAAAQQ6TSAbHGkSNFXCZJL39S896aST0oEHHpiuuuqqdNdddy1o+7/1W7+Vjj322PT1r389/dd//dcg4TXh/esnx+bYbgRHhjuOzPfzk+Tm90y2DvHE0/isEuZzeA4XXNQFP/IAD/AAD/AADyzGA+0vbNqfhgxf+buY7atLdalunJ94gAd4gAe25oH2PMS8gy/4gi+25gvP8wM/8AMPzO2BbHCk/UHPJJaPOuqodMwxx6Qbbrghfe9731vwJk888cR0yCGHpM997nPpwQcfXPDrJvmDERzRcWSSVG0LAQQQQAABBBBAAAEEEEBguRNof2HTfj+u/G0TsYwAAggggAACkyLQnoeYd0yKrO0ggAACCCCAQCkEssGRSSdu9ttvv3T66aenhx9+uAmAbN68ecFXvJ999tlp7733Th/96EebP0ezevXqVL/+Zz/72YI6lkwiQRbBER1H5k4iTbpebA/nSYxbdaSO1JEEMQ/wAA/wAA/wAA8svQfaX9i0P1hx5a9xaBwu/Th0vjPOjDPjrFQPtOch5h18yId8WKoPvW/+4z/+W6wHssGR9gc9L2W5/mVnnnlmE/64/PLL07333rvgzdWv/Zu/+ZsmJLJ27dp09NFHpx122KF5/caNG9Ptt9+evvWtb6Xnn39+wdtczA9GcETHkcXQ8xoEEEAAAQQQQAABBBBAAIFpJdD+wqb9Pl352yZiGQEEEEAAAQQmRaA9DzHvmBRZ20EAAQQQQACBUghkgyOLTaTM9brDDz88HX/88emee+5JdXBknMTTnnvumc4555z+cXnsscfS//7v/6bddtst7bPPPql+I88880y69NJL03PPPbdkHUgiOKLjiMTeOPU713jweok/dcEjPMADPMADPMADPMAD0+SB9hc2/X/Azz5w5a96n6Z652/1rJ7N43igWx5oz0PMO7p1fIwXx8N503mTB3iAB7rvgWxwpP1Bz2KXV65cmf78z/88rVq1Kv3TP/1T86dqxtnWwQcfnN773vemZ599Nl1zzTXpgQce6L+8Do6cfPLJqf7TNT/+8Y/Tl7/85f663IMIguR+rr3+uuuuaz9lGQEEEEAAAQQQQAABBBBAAIFiCfzf+z8z73vvXXnWvOutRAABBBBAAAEEFkugPQ8x71gsSa9DAAEEEEBg8QTWrVu3+Bd75ctOIBscqRNgcesngaon6mdjeSHrjzjiiHTcccelhx56KH22Co6M+/rtt98+7brrrk1w5Bd1R5HW799vv/3SmjVrmn36+CWXpJ/9/OcL2j/BkTh67hFAAAEEEEAAAQQQQAABBBBYPIH2FzbtLfkCp03EMgIIIIAAAghMikB7HmLeMSmytoMAAggggMDCCdx6660L+n5+UvmDes8ir9CrHrfzA9aPwafXa/7CS82z4XbQQQdVx6lGWoGt7jdv3tw8nsT//viP/zjtu+++6Zvf/Gb6z//8z0lscott1B1N6j9d86UvfSndfffdW6yfxBMRNLnwwgsnsTnbQAABBBBAAAEEEEAAAQQQQGAqCLRbxLff1G1/fWT7KcsIIIAAAggggMBECLTnIeYdE8FqIwgggAACCCBQEIEFdRzpJ3WqhEkdKBl3ea+99krnnHNO89qPf/zj6ZlnnlnUdnK/97TTTku/+qu/mr7xjW+kW265Zez9zG2/Xh/BkQsuuGBJtr8YvgvZb9sdv25xXdx4xw03vuEbHuABHuABHuABHijTA+0vbNqfraz7qyP8O3qRn6vwKq/yapleddwdd/5fuP/b8xDzDuPH+Fn4+HG+MV6MF+OFB3ig9kA2ONL+oGcxy7/zO7+T3vKWt6T77rsvXXbZZYvZRDrqqKPSTjvtlL7//e+np59+es5tRMeRa665Jq1fv37On3mpT0ZwRMeRl0rS6xFAAAEEEEAAAQQQQAABBKaJQPsLm/Z7c+Vvm4hlBBBAAAEEEJgUgfY8xLxjUmRtBwEEEEAAAQRKIZANjkwiYbRmzZr0mte8Jt14443phhtuWNQVRieddFJ6wxvekK6//vr0gx/8YIuOJXVXkz/5kz9pjtsnPvGJ9NRTTy3q9+TebwRHdByRvJLAlMDM+cJ6nuAJnuABHuABHuABHijJA+0vbNofrLjy13goaTzwv3pX7+aBPLBtPdCeh5h3bFv+6h1v5z3nPR7gAR5Y/h7IBkfaH/QsZvn8889Pq1atStdee22644475t1EHQA5+uijm5/57//+73TPPfc0jw855JB04oknpk2bNqUrrrgi/eQnP+lvp+5E8r73vS/tu+++ad26dem6667rr5v0gwiO6DgyabK2hwACCCCAAAIIIIAAAgggsJwJtL+wab8XV/62iVhGAAEEEEAAgUkRaM9DzDsmRdZ2EEAAAQQQQKAUAsPBkf8PAAD//zTNfkAAAEAASURBVOzdf+x8V13n8fNZKFvbbqymVUBpaGvWWEy/35iiCAGS3YWsIdJGWyRqGxtdNU34ixX9iw38scb9xhg3cSMmQjZBUhITEiigEZqGFFf5tqW7NBhRbAzFCN1FirS0pa1773y+5/OZeffM585n5t65c+c8Jvl+53POvXPuPc/zer/uj3nfMwc/9EM/9C/NK7Wv9v25556bvR8cHKz9fskll6T//J//86zN97znPekf//Ef00ntvfzlL0+33XbbbP2PfvSj6b777jta/5ZbbknXXXfdbF8efvjh9Oijj6bLLrssXXvtteniiy+eld/3vvelJ598cu39bft90v69853vnO3bu971rhPX62rH8pM544PPSXFIH/RBH+sfl8WP+BE/4ocP8AE+wAeG8oEz/+0zs+vlZf89+GuvdB29wf2VocZNu44LjguOC3yAD+yDD8TzEOcddL0PuubPdEzHztP4AB/Ypg/8q3/1r2b3bdr7OgelxJFlN3xWrb/66qvTrbfeOkvk+M3f/M30zDPPnPjRvH67Ups4cv/99x+t3+7sDTfckF772temSy+99Kj+8ccfTw8++GC655570rPPPntUP8QfOXHk3e9+9xDNaxMBBBBAAAEEEEAAAQQQQACBSRKIX9jETvzvd/xorFJGAAEEEEAAAQR6IRDPQ5x39IJVIwgggAACCCBQEYHOxJE2kym/jjJamoq2NpfHWH7Zv/k36fLLL09f+9rXUps40r7y/hw0fw+1fzlxZH7GkdnGt7T9WWbZgP3TfpO5hu9g8UNf9CW+hjs+iS/xJb7E11Dnv/yFv/AX/rKqv8QvbPK1cn5vn/zNr21cv/Mv/sW/+Neq/tX6RX7xpwtP9jZA8Du+30wfhwR2OT7Onjufh2n2Pj/jSF6wy/vf7qP94z/OX52/On91/uH86/h46Ph9SGBr5wfNDKmdiSN5ULwfEsiJI2YcoQgEEEAAAQQQQAABBBBAAAEEjgl0JY548veYlb8QQAABBBBAoF8C8TzEeUe/fLWGAAIIIIAAAvtPoDNxZJbh6Dd4Zz+102b05MSR+RlHjjJ9cDriRDdNZiw90EPzxBId+A1CfigO+AAf4AN8gA/wgVp8IH5hE2+pzD/5Ky7ERS1xoZ/OA/gdv+MD2/GBeB7ivGM73OkbZ8c5xzk+wAf4wP74QGfiSLzRU3s5J46YcaR2Jeg/AggggAACCCCAAAIIIIDAPIH4hc38svZvT/5GIsoIIIAAAggg0BeBeB7ivKMvstpBAAEEEEAAgVoIdCaOyBRbzBTLiSNmHFnkQid4yCjcn4xC8SyexbN45gN8gA/wAT7AB9bxgfiFTbyx4slfulpHV/yIbujGeQkf4AOr+EA8D3HeQTer6Ia/0AmdOM/gA3yADxz7QGfiSLzRU3s5J46YcaR2Jeg/AggggAACCCCAAAIIIIDAPIH4hc38svZvT/5GIsoIIIAAAggg0BeBeB7ivKMvstpBAAEEEEAAgVoIdCaOyLRazLTKiSNmHFnkQid4yMg7zsgTD+JBPIgHPsAH+AAf4AN8oEYfiF/YxBsrnvwVFzXGheMB3dO980I+sB0fiOchzju2w52+cXacc5zjA3yAD+yPD3QmjsQbPbWXc+KIGUdqV4L+I4AAAggggAACCCCAAAIIzBOIX9jML2v/9uRvJKKMAAJDE4i+xIeGJq59BMYjIN7HY2/LCCCAAAIIILAfBDoTR9pMsfw6yhhqKtraXK5peU4cmZ9xpKb+zzIHKx5//W8yJ41/tf5H//Qv/us9/xH/4l/8i/9ar//43+n8L35hk6+V83v75G9+5fsJ/IW/8Jc6769ty1/PnDufbWf2vjADQVNDf/SXj0dZKLns+DS9+Dh7Qrwb30MC9H3hiXD+7/jXaCDHA3/gDy2BrAfH/+kd/41fj/o9OEidiSOHluH/TCAnjphxJBPxjgACCCCAAAIIIIAAAggggEBKXYkjnvSnEgQQ2DaB6Et8aNsjYHsIbI+AeN8ea1tCAAEEEEAAgf0k0Jk4MnsCoMkw8d48adVwyIkj8zOOHGWi4UQnzQw99MAv+KU44AN8gA/wAT7AB/gAH6jRB+IXNvE2ysKT/q6fXT+7fnb/YAs+EH2JDzk+13h8ruW8VLyLb/HtOrwWv9NPfsfv+N1QPtCZOBJv9NRezokjZhypXQn6jwACCCCAAAIIIIAAAgggME8gfmEzv6z925P+kYgyAggMTSD6Eh8amrj2ERiPgHgfj70tI4AAAggggMB+EOhMHBkqY2Wq7ebEETOOyOiT0Sejb6o+Zr/5F//iX3yAD/ABPsAH+MAQPhC/sIm3TTzpT3dD6I6f0dVJuoq+xIfo5SS98JNp60O8T3v8xJ/x48/uU/ABPsAHxveBzsSReKOn9nJOHDHjSO1K0H8EEEAAAQQQQAABBBBAAIF5AvELm/ll7d+e9I9ElBFAYGgC0Zf40NDEtY/AeATE+3jsbRkBBBBAAAEE9oNAZ+KIDK/FDK+cOGLGkUUudIKHTMDxMwHFoTgUh+KQD/ABPsAH+AAfGNMH4hc28baJJ/3pc0x98sc69Rd9iQ/VqQPxX8e4i/c6xlk8G2fnk+578AE+wAeG84HOxJF4o6f2ck4cMeNI7UrQfwQQQAABBBBAAAEEEEAAgXkC8Qub+WXt3570j0SUEUBgaALRl/jQ0MS1j8B4BMT7eOxtGQEEEEAAAQT2g0Bn4kibuZVfRxk8TUVbm8s1Lc+JI/MzjtTU/1kmX8Xjr/9NJqPxr9b/6J/+xX+95z/iX/yLf/Ff6/Uf/zud/8UvbPK1cn5vn/TPr3w/gb/wF/5S5/21bfnrmXPns+3M3hdmHGlq6I/+8vEoCyWXHZ+mFx9nT4h343tIgL4vPKHN/x3/Gg3keOAP/KElkPXg+D+947/x61G/BwepM3Hk0DL8nwnkxBEzjmQi3hFAAAEEEEAAAQQQQAABBBBIqStxxJP+VIIAAtsmEH2JD217BGwPge0REO/bY21LCCCAAAIIILCfBDoTR2ZPADQZJt6bJ60aDjlxZH7GkaNMNJzopJmhhx74Bb8UB3yAD/ABPsAH+AAf4AM1+kD8wibeRll40t/1s+tn18/uH2zBB6Iv8SHH5xqPz7Wcl4p38S2+XYfX4nf6ye/4Hb8bygc6E0fijZ7ayzlxxIwjtStB/xFAAAEEEEAAAQQQQAABBOYJxC9s5pe1f3vSPxJRRgCBoQlEX+JDQxPXPgLjERDv47G3ZQQQQAABBBDYDwKdiSNDZaxMtd2cOGLGERl9Mvpk9E3Vx+w3/+Jf/IsP8AE+wAf4AB8YwgfiFzbxtokn/eluCN3xM7o6SVfRl/gQvZykF34ybX2I92mPn/gzfvzZfQo+wAf4wPg+0Jk4Em/01F7OiSNmHKldCfqPAAIIIIAAAggggAACCCAwTyB+YTO/rP3bk/6RiDICCAxNIPoSHxqauPYRGI+AeB+PvS0jgAACCCCAwH4Q6EwckeG1mOGVE0fMOLLIhU7wkAk4fiagOBSH4lAc8gE+wAf4AB/gA2P6QPzCJt428aQ/fY6pT/5Yp/6iL/GhOnUg/usYd/FexziLZ+PsfNJ9Dz7AB/jAcD7QmTgSb/TUXs6JI2YcqV0J+o8AAggggAACCCCAAAIIIDBPIH5hM7+s/duT/pGIMgIIDE0g+hIfGpq49hEYj4B4H4+9LSOAAAIIIIDAfhDoTBxpM7fy6yiDp6loa3O5puU5cWR+xpGa+j/L5Kt4/PW/yWQ0/tX6H/3Tv/iv9/xH/It/8S/+a73+43+n87/4hU2+Vs7v7ZP++ZXvJ/AX/sJf6ry/ti1/PXPufLad2fvCjCNNDf3RXz4eZaHksuPT9OLj7AnxbnwPCdD3hSe0+b/jX6OBHA/8gT+0BLIeHP+nd/w3fj3q9+AgdSaOHFqG/zOBnDhixpFMxDsCCCCAAAIIIIAAAggggAACKXUljnjSn0oQQGDbBKIv8aFtj4DtIbA9AuJ9e6xtCQEEEEAAAQT2k0Bn4sjsCYAmw8R786RVwyEnjszPOHKUiYYTnTQz9NADv+CX4oAP8AE+wAf4AB/gA3ygRh+IX9jE2ygLT/q7fnb97PrZ/YMt+ED0JT7k+Fzj8bmW81LxLr7Ft+vwWvxOP/kdv+N3Q/lAZ+JIvNFTezknjphxpHYl6D8CCCCAAAIIIIAAAggggMA8gfiFzfyy9m9P+kciygggMDSB6Et8aGji2kdgPALifTz2towAAggggAAC+0GgM3FkqIyVqbabE0fMOCKjT0afjL6p+pj95l/8i3/xAT7AB/gAH+ADQ/hA/MIm3jbxpD/dDaE7fkZXJ+kq+hIfopeT9MJPpq0P8T7t8RN/xo8/u0/BB/gAHxjfBzoTR+KNntrLOXHEjCO1K0H/EUAAAQQQQAABBBBAAAEE5gnEL2zml7V/e9I/ElFGAIGhCURf4kNDE9c+AuMREO/jsbdlBBBAAAEEENgPAp2JIzK8FjO8cuKIGUcWudAJHjIBx88EFIfiUByKQz7AB/gAH+ADfGBMH4hf2MTbJp70p88x9ckf69Rf9CU+VKcOxH8d4y7e6xhn8WycnU+678EH+AAfGM4HOhNH4o2e2ss5ccSMI7UrQf8RQAABBBBAAAEEEEAAAQTmCcQvbOaXtX970j8SUUYAgaEJRF/iQ0MT1z4C4xEQ7+Oxt2UEEEAAAQQQ2A8CnYkjbeZWfh1l8DQVbW0u17Q8J47MzzhSU/9nmXwVj7/+N5mMxr9a/6N/+hf/9Z7/iH/xL/7Ff63Xf/zvdP4Xv7DJ18r5vX3SP7/y/QT+wl/4S53317blr2fOnc+2M3tfmHGkqaE/+svHoyyUXHZ8ml58nD0h3o3vIQH6vvCENv93/Gs0kOOBP/CHlkDWg+P/9I7/xq9H/R4cpM7EkUPL8H8mkBNHzDiSiXhHAAEEEEAAAQQQQAABBBBAIKWuxBFP+lMJAghsm0D0JT607RGwPQS2R0C8b4+1LSGAAAIIIIDAfhLoTByZPQHQZJh4b560ajjkxJH5GUeOMtFwopNmhh564Bf8UhzwAT7AB/gAH+ADfIAP1OgD8QubeBtl4Ul/18+un10/u3+wBR+IvsSHHJ9rPD7Xcl4q3sW3+HYdXovf6Se/43f8bigf6EwciTd6ai/nxBEzjtSuBP1HAAEEEEAAAQQQQAABBBCYJxC/sJlf1v7tSf9IRBkBBIYmEH2JDw1NXPsIjEdAvI/H3pYRQAABBBBAYD8IdCaODJWxMtV2c+KIGUdk9Mnok9E3VR+z3/yLf/EvPsAH+AAf4AN8YAgfiF/YxNsmnvSnuyF0x8/o6iRdRV/iQ/Rykl74ybT1Id6nPX7iz/jxZ/cp+AAf4APj+0Bn4ki80VN7OSeOmHGkdiXoPwIIIIAAAggggAACCCCAwDyB+IXN/LL2b0/6RyLKCCAwNIHoS3xoaOLaR2A8AuJ9PPa2jAACCCCAAAL7QaAzcUSG12KGV04cMePIIhc6wUMm4PiZgOJQHIpDccgH+AAf4AN8gA+M6QPxC5t428ST/vQ5pj75Y536i77Eh+rUgfivY9zFex3jLJ6Ns/NJ9z34AB/gA8P5QGfiSLzRU3s5J46YcaR2Jeg/AggggAACCCCAAAIIIIDAPIH4hc38svZvT/pHIsoIIDA0gehLfGho4tpHYDwC4n089raMAAIIIIAAAvtBoDNxpM3cyq+jDJ6moq3N5ZqW58SR+RlHaur/LJOv4vHX/yaT0fhX63/0T//iv97zH/Ev/sW/+K/1+o//nc7/4hc2+Vo5v7dP+udXvp/AX/gLf6nz/tq2/PXMufPZdmbvCzOONDX0R3/5eJSFksuOT9OLj7MnxLvxPSRA3xee0Ob/jn+NBnI88Af+0BLIenD8n97x3/j1qN+Dg9SZOHJoGf7PBHLiiBlHMhHvCCCAAAIIIIAAAggggAACCKTUlTjiSX8qQQCBbROIvsSHtj0CtofA9giI9+2xtiUEEEAAAQQQ2E8CnYkjsycAmgwT782TVg2HnDgyP+PIUSYaTnTSzNBDD/yCX4oDPsAH+AAf4AN8gA/wgRp9IH5hE2+jLDzp7/rZ9bPrZ/cPtuAD0Zf4kONzjcfnWs5Lxbv4Ft+uw2vxO/3kd/yO3w3lA52JI/FGT+3lnDhixpHalaD/CCCAAAIIIIAAAggggAAC8wTiFzbzy9q/PekfiSgjgMDQBKIv8aGhiWsfgfEIiPfx2NsyAggggAACCOwHgc7EkaEyVqbabk4cMeOIjD4ZfTL6pupj9pt/8S/+xQf4AB/gA3yADwzhA/ELm3jbxJP+dDeE7vgZXZ2kq+hLfIheTtILP5m2PsT7tMdP/Bk//uw+BR/gA3xgfB/oTByJN3pqL+fEETOO1K4E/UcAAQQQQAABBBBAAAEEEJgnEL+wmV/W/u1J/0hEGQEEhiYQfYkPDU1c+wiMR0C8j8felhFAAAEEEEBgPwh0Jo7I8FrM8MqJI2YcWeRCJ3jIBBw/E1AcikNxKA75AB/gA3yAD/CBMX0gfmETb5t40p8+x9Qnf6xTf9GX+FCdOhD/dYy7eK9jnMWzcXY+6b4HH+ADfGA4H+hMHIk3emov58QRM47UrgT9RwABBBBAAAEEEEAAAQQQmCcQv7CZX9b+7Un/SEQZAQSGJhB9iQ8NTVz7CIxHQLyPx96WEUAAAQQQQGA/CHQmjrSZW/l1lMHTVLS1uVzT8pw4Mj/jSE39n2XyVTz++t9kMhr/av2P/ulf/Nd7/iP+xb/4F/+1Xv/xv9P5X/zCJl8r5/f2Sf/8yvcT+At/4S913l/blr+eOXc+287sfWHGkaaG/ugvH4+yUHLZ8Wl68XH2hHg3vocE6PvCE9r83/Gv0UCOB/7AH1oCWQ+O/9M7/hu/HvV7cJA6E0cOLcP/mUBOHDHjSCbiHQEEEEAAAQQQQAABBBBAAIGUuhJHPOlPJQggsG0C0Zf40LZHwPYQ2B4B8b491raEAAIIIIAAAvtJoDNxZPYEQJNh4r150qrhkBNH5mccOcpEw4lOmhl66IFf8EtxwAf4AB/gA3yAD/ABPlCjD8QvbOJtlIUn/V0/u352/ez+wRZ8IPoSH3J8rvH4XMt5qXgX3+LbdXgtfqef/I7f8buhfKAzcSTe6Km9nBNHzDhSuxL0HwEEEEAAAQQQQAABBBBAYJ5A/MJmfln7tyf9IxFlBBAYmkD0JT40NHHtIzAeAfE+HntbRgABBBBAAIH9INCZODJUxspU282JI2YckdEno09G31R9zH7zL/7Fv/gAH+ADfIAP8IEhfCB+YRNvm3jSn+6G0B0/o6uTdBV9iQ/Ry0l64SfT1od4n/b4iT/jx5/dp+ADfIAPjO8DnYkj8UZP7eWcOGLGkdqVoP8IIIAAAggggAACCCCAAALzBOIXNvPL2r896R+JKCOAwNAEoi/xoaGJax+B8QiI9/HY2zICCCCAAAII7AeBzsQRGV6LGV45ccSMI4tc6AQPmYDjZwKKQ3EoDsUhH+ADfIAP8AE+MKYPxC9s4m0TT/rT55j65I916i/6Eh+qUwfiv45xF+91jLN4Ns7OJ9334AN8gA8M5wOdiSPxRk/t5Zw4YsaR2pWg/wgggAACCCCAAAIIIIAAAvME4hc288vavz3pH4koI4DA0ASiL/GhoYlrH4HxCIj38djbMgIIIIAAAgjsB4HOxJE2cyu/jjJ4moq2NpdrWp4TR+ZnHKmp/7NMvorHX/+bTEbjX63/0T/9i/96z3/Ev/gX/+K/1us//nc6/4tf2ORr5fzePumfX/l+An/hL/ylzvtr2/LXM+fOZ9uZvS/MONLU0B/95eNRFkouOz5NLz7OnhDvxveQAH1feEKb/zv+NRrI8cAf+ENLIOvB8X96x3/j16N+Dw5SZ+LIoWX4PxPIiSNmHMlEvCOAAAIIIIAAAggggAACCCCQUlfiiCf9qQQBBLZNIPoSH9r2CNgeAtsjIN63x9qWEEAAAQQQQGA/CXQmjsyeAGgyTLw3T1o1HHLiyPyMI0eZaDjRSTNDDz3wC34pDvgAH+ADfIAP8AE+wAdq9IH4hU28jbLwpL/rZ9fPrp/dP9iCD0Rf4kOOzzUen2s5LxXv4lt8uw6vxe/0k9/xO343lA90Jo7EGz2nLb/lLW9JL3rRi5Z+7KGHHkoPPvjg0uVxwQtf+ML0mte8Jl177bXpyiuvTM8++2z66le/mv7qr/4q3XfffbMbL/EzfZZz4ogZR/qkqi0EEEAAAQQQQAABBBBAAIGpE4hf2MT+eNI/ElFGAIGhCURf4kNDE9c+AuMREO/jsbdlBBBAAAEEENgPAp2JI5tkrFx22WXp7W9/+4mk7r333nT33Xev9KTNJZdckm6//fZ0xRVXzNp87LHH0kUXXZTa+vb18MMPp/e///2ztjbZ75MytXLiiBlHZPSdpJOh9KdduqM7maR8gA/wAT7AB/gAH+ADu+oD8Qub2YX63H+e9Odf/It/bdu/oi/xIT7Eh/bXh8S7+Bbf+xvf2z5/sD1+wk/4Sa0+0Jk4MneP59R/XnPNNennf/7n02c/+9n0iU98ovj5b3/72+mZZ54pLouVN910U7r++uvT17/+9XTnnXfOZhpp12m3c/PNN6eLL754loTSJqMM9cqJI2YcGYqwdhFAAAEEEEAAAQQQQAABBKZIIH5hE/vgSf9IRBkBBIYmEH2JDw1NXPsIjEdAvI/H3pYRQAABBBBAYD8IdCaObJJR8+pXvzq94Q1vSHfddVe6//77N/rt1nZWkXb2kjbL673vfW/60pe+tNDemTNn0o033pieeOKJ9Nu//dvpueeeW1i+ST/mM8ty4ogZR2QczuuiL31ph67oSiYrH+ADfIAP8AE+wAf4wFR9IH5hE2+beNKfv/E3/rZtf4u+xIf4EB/aXx8S7+JbfO9vfG/7/MH2+Ak/4Se1+kBn4ki80XOacpvI0SZ0/OEf/mH68pe/fJqPPm/dnBjyla98Jb3nPe953vK2I7/+678+++maNrHkkUceed46fVTkxBEzjvRBUxsIIIAAAggggAACCCCAAAL7QiB+YRP75Un/SEQZAQSGJhB9iQ8NTVz7CIxHQLyPx96WEUAAAQQQQGA/CHQmjrQZNfl1lGHVVLS1ubxs+a/8yq+k7/3e703/9b/+1/Tss8+myy+/fDYTyDcee2ylz8+3/8Y3vjH9+I//eHrggQfSXR/5SPHzt99+e7rqqqvSR5rl7Xrzn2/3MZcPmr9X2f+8/nz/cuLI/Iwj88tnGUgbtO/zTSYjfmvrk37oR/ys7+/iR/yIH/Gz7vkh/+Af/IN/8I/D6+34hU2+Vs7v7ZP++ZWvt8WP+BE/x/erxMchgT794cy58xnr7H1hxpGmhv7oL+stCyWXHZ+mFx9nT4h343tIgL4vPEHP/x3/Gg3keOAP/KElkPXg+D+947/x61G/za++dCaOHFrG6f9vG/6N3/iN9I1vfCOdP38+vf71r08XX3zxrKGnn346PfTQQ+kTn/hEevLJJ1dq/Oabb07XXXdduueee9KnPvWp4md+6qd+Kv3wD//wbHm73hCvnDhixpEh6GoTAQQQQAABBBBAAAEEEEBgqgS6Ekc86T/VkbXfCEyXQPQlPjTdsbTnCHQREO9dhCxHAAEEEEAAAQROJtCZODJ7grLJMDnt+xVXXJHuuOOOo60/+uijqf2Zme/6ru9KL3nJS2YZK9/85jfT7//+76cnnniis/2f/dmfTT/wAz+Q/uRP/iR95jOfKa7/pje9Kd1www3pL/7iL9Kf/umfHmeIrbH/y/qbE0fmZxw5ykTrcTvLtq++eaIV56L+cTm9T4kn8SRuxA0f4AN8gA/wAT7AB/rzgfiFzdENgQt/LDzp77rOdV0zw6346y/+HM/Keoq+xIfKnOgHl33wY/FOx/ugY35Mx3Ts/JgP8IExfaAzcSTe6Fm1/IpXvCL99E//dHr88cfTBz/4wfTII48cfbRNHLnllltmP13z+c9/Pv3xH//x0bJlf9x6663p6quvTh//+MdnM5iU1vuJn/iJ9MpXvnK2vF1vlVdOBFll3fl1PvzhD88X/Y0AAggggAACCCCAAAIIIIBA1QT+5Wf/4MT+H3zgl09cbiECCCDQN4HoS3yob8LaQ2B3CIj33RkLe4IAAgggUC+BBx98sN7O70HPOxNH1s1suuiii9J3fud3zhJHvvWtbz3vSZqXvexl6Rd+4RdmT5f8zu/8zuwnbU7KoPm5n/u5dO21185mEvnLv/zL57XX7udP/uRPph/5kR9J7fJ2ZpKT2sv9kjiyByrWBQQQQAABBBBAAAEEEEAAgdEJxC9s4g75wjYSUUYAgaEJRF/iQ0MT1z4C4xEQ7+Oxt2UEEEAAAQQygc9+9rMrfT+fv6f3vlszrHQmjuSBHuL9bW972+yna/7oj/4offGLXzxxEz/zMz+TfvAHfzDdfffd6d577y2ue9NNN6Xrr78+ffrTn06f/OQni+tsWpkTTd797ndv2pTPI4AAAggggAACCCCAAAIIILA3BOIU8bFj//sdPxqrlBFAAIFBCURf4kOD4tY4AqMSEO+j4rdxBBBAAAEEENgDAp2JI0Nm+tx2221HPz/zmc985sQMpPwzNA888EC66667ijOO3H777emqq66azTbSzjqyyowjp+1fThx517veNUj7p90f6+9WJpbxMB5D+A5d0RVd+W1LPsAH+AAf4AN8YAo+EL+wifdMHvy1V7qOPuBn/IyfbdPPoi/xIfrbpv743Xb1Jt63y5u+8eanzuv5AB/gA/vnA52JI/FGz6rl173udemyyy6bzf7x2GOPFT+WZxz54Ac/mP76r/+6uE6ufNWrXpXe+MY3pkceeSS9973vzdUL7+94xzvSxRdfnD7wgQ+kv/3bv11Y1lchJ46YcaQvotpBAAEEEEAAAQQQQAABBBDYBwLxC5vYJ0/6RyLKCCAwNIHoS3xoaOLaR2A8AuJ9PPa2jAACCCCAAAL7QaAzcaTNGMuvo8yhpqKtzeXS8ptvuSVdd9116c/+7M/Sn//5n89WyesfNKUrv+d70q/+6q/O6n/3d383tckl88tj+1deeWW644470nPPPZf+4D3vSV/56leP1m8baX/G5q1vfWt65pln0rn/9t/S09/+9sLydp2T2l91eU4cmZ9xpP1s++qj/VmGXtNW7L/28aWvC5l74oM/zPlt643tS3yID8fPJsO9iQXnD8d+MDMH/sAf85P94oM/zPkBfzgkMMT5U/zCJrPO7+2T/vk1xPbXvX/h+On4mfVIn4cEMo99OL88c+58HtbZ+8KMI02N+Bf/We9ZKLm8D/qvTd9nT4h343tIgL7dP3P/zP0zxzfnf7WdH7RHQMe/FY9/zX3UzsSRfFJ12vczZ86kG2+8MX27SeB4//vfn770pS8dNdHORNImebz0pS9NDz74YPrwhz98tKxNEHn9618/K7c/S/N3f/d3R8t+6Zd+afaZv//7v0933nlneuqpp2bLLr300vSLv/iL6fLLL0+f+9zn0oc+9KGjz/T9R04cMeNI32S1hwACCCCAAAIIIIAAAgggMGUCXYkjnvSf8ujadwSmSSD6Eh+a5jjaawRWISDeV6FkHQQQQAABBBBAYDmBzsSRWQZiflLvlO+3XJh1pG3j4YcfTo8++ujs52uuvfba2U/KtOX3ve996cknn0x5Oy9/+cvTbbfdNtvjj370o+m+++47ygR68YtfPEsQeeELXzibWeQLX/hCetGLXpSuueaaWQbME088kX7v934vfetb3zpqL7fb13tOHJmfceQoU+mUfHzuQoYTboPptS/da8dv1fErfsUH+AAf4AN8gA/wAT7Q5QPxC5t4K2LhSX/Xga4Dm3tFfIWvdPnKpsujL/Ehccd39td3xLv4Ft/7G9+bng/4PH/gD/yBD6zmA52JI/FGz2nKbeM33HBDeu1rX5vaWUHy6/HHH5/NNHLPPfekZ599NlfP3q+++up06623zv5uE0fuv//+heXtjCRvetOb0ste9rLZDYZ2YfvzNX/zN3+TPvaxj6V//ud/Xli/70JOHDHjSN9ktYcAAggggAACCCCAAAIIIDBlAvELm9gXT/pHIsoIIDA0gehLfGho4tpHYDwC4n089raMAAIIIIAAAvtBoDNxpK8MnPbnadqfkvna176W2plBNm33BS94QWqTSJ555pn0T//0T7P3bWSM5cQRM46slpm06Tj7PM7biGs6ozM6k3HMB/gAH+ADfIAP8IHNfSB+YRNvm3jSX5yJs83jzPHqdHEUfYkPnY4fveE1Jd8W7/Q6Jb3yV3qlV+fFfIAP7KIPdCaOxBs9tZdz4ogZR2pXgv4jgAACCCCAAAIIIIAAAgjME4hf2Mwva//2pH8koowAAkMTiL7Eh4Ymrn0ExiMg3sdjb8sIIIAAAgggsB8EOhNHZDwtZjzlxBEzjixyoRM8djEzji7pki5lbvMBPsAH+AAf4AN8YHs+EL+wibdNPOkvHsXj9uLR8e8w3qIv8SE+xIf214fEu/gW3/sb385rxLf4Ft98YDs+0Jk4Em/01F7OiSNmHKldCfqPAAIIIIAAAggggAACCCAwTyB+YTO/rP3bk/6RiDICCAxNIPoSHxqauPYRGI+AeB+PvS0jgAACCCCAwH4Q6EwcaTN48usoo6upaGtzuablOXFkfsaRmvo/y+iqePz1v8loM/7V+h/907/4r/f8R/yLf/Ev/mu9/uN/p/O/+IVNvlbO7+2T/vmV7yfwF/7CX+q8v7Ytfz1z7ny2ndn7wowjTQ390V8+HmWh5LLj0/Ti4+wJ8W58DwnQ94Un9vm/41+jgRwP/IE/tASyHhz/p3f8N3496vfgIHUmjhxahv8zgZw4YsaRTMQ7AggggAACCCCAAAIIIIAAAil1JY540p9KEEBg2wSiL/GhbY+A7SGwPQLifXusbQkBBBBAAAEE9pNAZ+LI7AmAJsPEe/OkVcMhJ47MzzhylImGE500M/TQA7/gl+KAD/ABPsAH+AAf4AN8oEYfiF/YxNsoC0/6u352/ez62f2DLfhA9CU+5Phc4/G5lvNS8S6+xbfr8Fr8Tj/5Hb/jd0P5QGfiSLzRU3s5J46YcaR2Jeg/AggggAACCCCAAAIIIIDAPIH4hc38svZvT/pHIsoIIDA0gehLfGho4tpHYDwC4n089raMAAIIIIAAAvtBoDNxZKiMlam2mxNHzDgio09Gn4y+qfqY/eZf/It/8QE+wAf4AB/gA0P4QPzCJt428aQ/3Q2hO35GVyfpKvoSH6KXk/TCT6atD/E+7fETf8aPP7tPwQf4AB8Y3wc6E0fijZ7ayzlxxIwjtStB/xFAAAEEEEAAAQQQQAABBOYJxC9s5pe1f3vSPxJRRgCBoQlEX+JDQxPXPgLjERDv47G3ZQQQQAABBBDYDwKdiSMyvBYzvHLiiBlHFrnQCR4yAcfPBBSH4lAcikM+wAf4AB/gA3xgTB+IX9jE2yae9KfPMfXJH+vUX/QlPlSnDsR/HeMu3usYZ/FsnJ1Puu/BB/gAHxjOBzoTR+KNntrLOXHEjCO1K0H/EUAAAQQQQAABBBBAAAEE5gnEL2zml7V/e9I/ElFGAIGhCURf4kNDE9c+AuMREO/jsbdlBBBAAAEEENgPAp2JI23mVn4dZfA0FW1tLte0PCeOzM84UlP/Z5l8FY+//jeZjMa/Wv+jf/oX//We/4h/8S/+xX+t13/873T+F7+wydfK+b190j+/8v0E/sJf+Eud99e25a9nzp3PtjN7X5hxpKmhP/rLx6MslFx2fJpefJw9Id6N7yEB+r7whDb/d/xrNJDjgT/wh5ZA1oPj//SO/8avR/0eHKTOxJFDy/B/JpATR8w4kol4RwABBBBAAAEEEEAAAQQQQCClrsQRT/pTCQIIbJtA9CU+tO0RsD0EtkdAvG+PtS0hgAACCCCAwH4S6EwcmT0B0GSYeG+etGo45MSR+RlHjjLRcKKTZoYeeuAX/FIc8AE+wAf4AB/gA3yAD9ToA/ELm3gbZeFJf9fPrp9dP7t/sAUfiL7Ehxyfazw+13JeKt7Ft/h2HV6L3+knv+N3/G4oH+hMHIk3emov58QRM47UrgT9RwABBBBAAAEEEEAAAQQQmCcQv7CZX9b+7Un/SEQZAQSGJhB9iQ8NTVz7CIxHQLyPx96WEUAAAQQQQGA/CHQmjgyVsTLVdnPiiBlHZPTJ6JPRN1Ufs9/8i3/xLz7AB/gAH+ADfGAIH4hf2MTbJp70p7shdMfP6OokXUVf4kP0cpJe+Mm09SHepz1+4s/48Wf3KfgAH+AD4/tAZ+JIvNFTezknjphxpHYl6D8CCCCAAAIIIIAAAggggMA8gfiFzfyy9m9P+kciygggMDSB6Et8aGji2kdgPALifTz2towAAggggAAC+0GgM3FEhtdihldOHDHjyCIXOsFDJuD4mYDiUByKQ3HIB/gAH+ADfIAPjOkD8QubeNvEk/70OaY++WOd+ou+xIfq1IH4r2PcxXsd4yyejbPzSfc9+AAf4APD+UBn4ki80VN7OSeOmHGkdiXoPwIIIIAAAggggAACCCCAwDyB+IXN/LL2b0/6RyLKCCAwNIHoS3xoaOLaR2A8AuJ9PPa2jAACCCCAAAL7QaAzcaTN3MqvowyepqKtzeWalufEkfkZR2rq/yyTr+Lx1/8mk9H4V+t/9E//4r/e8x/xL/7Fv/iv9fqP/53O/+IXNvlaOb+3T/rnV76fwF/4C3+p8/7atvz1zLnz2XZm7wszjjQ19Ed/+XiUhZLLjk/Ti4+zJ8S78T0kQN8XntDm/45/jQZyPPAH/tASyHpw/J/e8d/49ajfg4PUmThyaBn+zwRy4ogZRzIR7wgggAACCCCAAAIIIIAAAgik1JU44kl/KkEAgW0TiL7Eh7Y9AraHwPYIiPftsbYlBBBAAAEEENhPAp2JI7MnAJoME+/Nk1YNh5w4Mj/jyFEmGk500szQQw/8gl+KAz7AB/gAH+ADfIAP8IEafSB+YRNvoyw86e/62fWz62f3D7bgA9GX+JDjc43H51rOS8W7+BbfrsNr8Tv95Hf8jt8N5QOdiSPxRk/t5Zw4YsaR2pWg/wgggAACCCCAAAIIIIAAAvME4hc288vavz3pH4koI4DA0ASiL/GhoYlrH4HxCIj38djbMgIIIIAAAgjsB4HOxJGhMlam2m5OHDHjiIw+GX0y+qbqY/abf/Ev/sUH+AAf4AN8gA8M4QPxC5t428ST/nQ3hO74GV2dpKvoS3yIXk7SCz+Ztj7E+7THT/wZP/7sPgUf4AN8YHwf6EwciTd6ai/nxBEzjtSuBP1HAAEEEEAAAQQQQAABBBCYJxC/sJlf1v7tSf9IRBkBBIYmEH2JDw1NXPsIjEdAvI/H3pYRQAABBBBAYD8IdCaOyPBazPDKiSNmHFnkQid4yAQcPxNQHIpDcSgO+QAf4AN8gA/wgTF9IH5hE2+beNKfPsfUJ3+sU3/Rl/hQnToQ/3WMu3ivY5zFs3F2Pum+Bx/gA3xgOB/oTByJN3pqL+fEETOO1K4E/UcAAQQQQAABBBBAAAEEEJgnEL+wmV/W/u1J/0hEGQEEhiYQfYkPDU1c+wiMR0C8j8felhFAAAEEEEBgPwh0Jo60mVv5dZTB01S0tblc0/KcODI/40hN/Z9l8lU8/vrfZDIa/2r9j/7pX/zXe/4j/sW/+Bf/tV7/8b/T+V/8wiZfK+f39kn//Mr3E/gLf+Evdd5f25a/njl3PtvO7H1hxpGmhv7oLx+PslBy2fFpevFx9oR4N76HBOj7whPa/N/xr9FAjgf+wB9aAlkPjv/TO/4bvx71e3CQOhNHDi3D/5lAThwx40gm4h0BBBBAAAEEEEAAAQQQQACBlLoSRzzpTyUIILBtAtGX+NC2R8D2ENgeAfG+Pda2hAACCCCAAAL7SaAzcWT2BECTYeK9edKq4ZATR+ZnHDnKRMOJTpoZeuiBX/BLccAH+AAf4AN8gA/wAT5Qow/EL2zibZSFJ/1dP7t+dv3s/sEWfCD6Eh9yfK7x+FzLeal4F9/i23V4LX6nn/yO3/G7oXygM3Ek3uipvZwTR8w4UrsS9B8BBBBAAAEEEEAAAQQQQGCeQPzCZn5Z+7cn/SMRZQQQGJpA9CU+NDRx7SMwHgHxPh57W0YAAQQQQACB/SDQmTgyVMbKVNvNiSNmHJHRJ6NPRt9Ufcx+8y/+xb/4AB/gA3yAD/CBIXwgfmETb5t40p/uhtAdP6Ork3QVfYkP0ctJeuEn09aHeJ/2+Ik/48ef3afgA3yAD4zvA52JI/FGT+3lnDhixpHalaD/CCCAAAIIIIAAAggggAAC8wTiFzbzy9q/PekfiSgjgMDQBKIv8aGhiWsfgfEIiPfx2NsyAggggAACCOwHgc7EERleixleOXHEjCOLXOgED5mA42cCikNxKA7FIR/gA3yAD/ABPjCmD8QvbOJtE0/60+eY+uSPdeov+hIfqlMH4r+OcRfvdYyzeDbOzifd9+ADfIAPDOcDnYkj8UZP7eWcOGLGkdqVoP8IIIAAAggggAACCCCAAALzBOIXNvPL2r896R+JKCOAwNAEoi/xoaGJax+B8QiI9/HY2zICCCCAAAII7AeBzsSRNnMrv44yeJqKtjaXa1qeE0fmZxypqf+zTL6Kx1//m0xG41+t/9E//Yv/es9/xL/4F//iv9brP/53Ov+LX9jka+X83j7pn1/5fgJ/4S/8pc77a9vy1zPnzmfbmb0vzDjS1NAf/eXjURZKLjs+TS8+zp4Q78b3kAB9X3hCm/87/jUayPHAH/hDSyDrwfF/esd/49ejfg8OUmfiyKFl+D8TyIkjZhzJRLwjgAACCCCAAAIIIIAAAgggkFJX4ogn/akEAQS2TSD6Eh/a9gjYHgLbIyDet8falhBAAAEEEEBgPwl0Jo7MngBoMky8N09aNRxy4sj8jCNHmWg40UkzQw898At+KQ74AB/gA3yAD/ABPsAHavSB+IVNvI2y8KS/62fXz66f3T/Ygg9EX+JDjs81Hp9rOS8V7+JbfLsOr8Xv9JPf8Tt+N5QPdCaOxBs9tZdz4ogZR2pXgv4jgAACCCCAAAIIIIAAAgjME4hf2Mwva//2pH8koowAAkMTiL7Eh4Ymrn0ExiMg3sdjb8sIIIAAAgggsB8EOhNHhspYmWq7OXHEjCMy+mT0yeibqo/Zb/7Fv/gXH+ADfIAP8AE+MIQPxC9s4m0TT/rT3RC642d0dZKuoi/xIXo5SS/8ZNr6EO/THj/xZ/z4s/sUfIAP8IHxfaAzcSTe6Km9nBNHzDhSuxL0HwEEEEAAAQQQQAABBBBAYJ5A/MJmfln7tyf9IxFlBBAYmkD0JT40NHHtIzAeAfE+HntbRgABBBBAAIH9INCZOCLDazHDKyeOmHFkkQud4CETcPxMQHEoDsWhOOQDfIAP8AE+wAfG9IH4hU28beJJf/ocU5/8sU79RV/iQ3XqQPzXMe7ivY5xFs/G2fmk+x58gA/wgeF8oDNxJN7oqb2cE0fMOFK7EvQfAQQQQAABBBBAAAEEEEBgnkD8wmZ+Wfu3J/0jEWUEEBiaQPQlPjQ0ce0jMB4B8T4ee1tGAAEEEEAAgf0g0Jk40mZu5ddRBk9T0dbmck3Lc+LI/IwjNfV/lslX8fjrf5PJaPyr9T/6p3/xX+/5j/gX/+Jf/Nd6/cf/Tud/8QubfK2c39sn/fMr30/gL/yFv9R5f21b/nrm3PlsO7P3hRlHmhr6o798PMpCyWXHp+nFx9kT4t34HhKg7wtPaPN/x79GAzke+AN/aAlkPTj+T+/4b/x61O/BQepMHDm0DP9nAjlxxIwjmYh3BBBAAAEEEEAAAQQQQAABBFLqShzxpD+VIIDAtglEX+JD2x4B20NgewTE+/ZY2xICCCCAAAII7CeBzsSR2RMATYaJ9+ZJq4ZDThyZn3HkKBMNJzppZuihB37BL8UBH+ADfIAP8AE+wAf4QI0+EL+wibdRFp70d/3s+tn1s/sHW/CB6Et8yPG5xuNzLeel4l18i2/X4bX4nX7yO37H74bygc7EkXijp/ZyThwx40jtStB/BBBAAAEEEEAAAQQQQACBeQLxC5v5Ze3fnvSPRJQRQGBoAtGX+NDQxLWPwHgExPt47G0ZAQQQQAABBPaDQGfiyFAZK1NtNyeOmHFERp+MPhl9U/Ux+82/+Bf/4gN8gA/wAT7AB4bwgfiFTbxt4kl/uhtCd/yMrk7SVfQlPkQvJ+mFn0xbH+J92uMn/owff3afgg/wAT4wvg90Jo7EGz21l3PiiBlHaleC/iOAAAIIIIAAAggggAACCMwTiF/YzC9r//akfySijAACQxOIvsSHhiaufQTGIyDex2NvywgggAACCCCwHwQ6E0dkeC1meOXEETOOLHKhEzxkAo6fCSgOxaE4FId8gA/wAT7AB/jAmD4Qv7CJt0086U+fY+qTP9apv+hLfKhOHYj/OsZdvNcxzuLZODufdN+DD/ABPjCcD3QmjsQbPbWXc+KIGUdqV4L+I4AAAggggAACCEyRQLyh7MnjKY6ifd5VAjG+4n6Kt0hEGQEEhiYQfYkPDU1c+wiMR0C8j8felhFAAAEEEEBgPwh0Jo60mVv5dZTB01S0tblc0/KcODI/40hN/Z9l8lU8/vrfZDIa/2r9j/7pX/zXe/4j/sX/PsV/vKE8e/LY+Y3zm0YDNV7f9u3vMb4arAuvNt7yK/PeJ39peeaX/l14AqoBUuv9o77ji77Wi68z587nsJy9L8w4Qp/is9FA9usslFx2fJqef589Id6N7yEB+nZ+4vzE/R3Ht+kd31oH59/8eyv+fXCQOhNH8kmV90MCOXHEjCMUgQACCCCAAAIIIIDA9AjEL7Y9eTy9MbTHu0sgxlfcU/EWiSgjgMDQBKIv8aGhiWsfgfEIiPfx2NsyAggggAACCOwHgc7EkVkGS5Nh4r3JRGw45MSR+RlHjjK9cKKT5gkzeuAX/FIc8AE+wAf4AB/YXR+IN5QXnjx2Pu983vn8RtczMb7ibRPx5vjo+Li7x8d9PX+NvsSH+BAf2l8fEu/iW3zvb3zv63mKfvEtvsW3ds0HOhNH4o2e2ss5ccSMI7UrQf8RQAABBBBAAAEEpkgg3lD25PEUR9E+7yqBGF9xP8VbJKKMAAJDE4i+xIeGJq59BMYjIN7HY2/LCCCAAAIIILAfBDoTR3Yt02Xs/cmJI2YckQkoE1Am4Nh+ZPt8iA/xIT7AB/jA6X0g3lD25LE4Ekenj6Nlx58YX/G2iXgTb+Ktv3hbFofqF+Ms+hIfWuRDL3jsky+Ld3reJz3zZ3qmZ+fNfIAPjOEDnYkj8UbPacv/+l//6/S6170uff/3f3968YtfnJ566qn0f//v/0333Xdf+vznP3/a5tJb3vKW9KIXvWjp5x566KH04IMPLl2+6YKcOGLGkU1J+jwCCCCAAAIIIIAAAtsnEG8oe/J4+2Ngi/tLIMZX7Kl4i0SUEUBgaALRl/jQ0MS1j8B4BMT7eOxtGQEEEEAAAQT2g0Bn4sgmGU0veclLZokel19++ey3sr/2ta+lSy65JH3Hd3zHjN4XvvCFdOedd87+XmU7l112WXr7299+Ivl777033X333YP9NndOHDHjiEyvMTK9VokT+yUTlU74Ex/gA3yAD/CB5T4Qbyh78li8iJfl8XLa40mMr3jxLt7Em3jrL95OG5+1rh99iQ/xIT60vz4k3sW3+N7f+K71PEa/+Rpf42vb9oHOxJF4o2fVcivmX/7lX07f+73fm774xS+mD33oQ+mJJ56YffwVr3hFevOb35wuuuii9MlPfjJ9+tOfXqnZa665Jv38z/98+uxnP5s+8YlPFD/z7W9/Oz3zzDPFZX1U5sQRM470QVMbCCCAAAIIIIAAAghsl0C8oezJ4+3yt7X9JhDjK/ZWvEUiygggMDSB6Et8aGji2kdgPALifTz2towAAggggAAC+0GgM3GkzWTJr6PMpqairc3l0vLrmuSQm2++OX3zm99Mv/u7vztL5sjrHzQf+NEf+7H0H//jf0yPPvpo+h//43/MmphfXmr/1a9+dXrDG96Q7rrrrnT//fefuP3S57vaX2V5ThyZn3Gk1P+htj/LLGo2qP2T9YcPPjmexechgcyj9V/xIT6yHsSH+GgJZD3wB/5Yy/Eh3lCePXncxEIt/V/3+g6fY788PHocl/nncfzE+Mqs8nsbb/nl+HPhyakGiPg6jif6OCQgPvqLjzPnzmdZzd4XZhxpasSf+MvxloWSy47v04uPsyfEu/E9JEDf/R1fHD8cP3I88Rf+0hLIenD+ML3zB+M3p99mUpDOxJHDkD/9///+3//79JrXvCb9+Z//eXF2kO/6ru9Kb3vb29Jzzz2Xfuu3fiu1M4V0vW688cZ05syZ9Id/+Ifpy1/+ctfqgyzPiSNmHBkEr0YRQAABBBBAAAEEEBiUQPxi25PHg+LWeGUEYnzF7ou3SEQZAQSGJhB9iQ8NTVz7CIxHQLyPx96WEUAAAQQQQGA/CHQmjqz72zk33XRTuv7669NHPvKR2U/LxHauuOKKdMcdd6Rnn302/eZv/ubs/Sgjq8loieu35V/5lV+Z/fRNu377czSXX375LPHkG9/4RnH9rvbWWZ4TR+ZnHFmnnVL/tFMed1xwES9+y48P8AE+wAf4AB/oywfiDeWFJ4+XXIfQH/31pb99byfGV7xtIt4cz/kpP922D0Zf4kN8iA/trw+Jd/Etvvc3vrd9/mB7/ISf8JNafaAzcSTe6Fm13AZV+2rBll4/+qM/Ovupmq985SvpPe95T2mVhbp2R3/jN34jtUki58+fT69//evTxRdfPFvn6aefTg899NBsZpMnn3xy4XN9F3LiiBlH+iarPQQQQAABBBBAAAEEhicQbyh78nh45rZQD4EYX7Hn4i0SUUYAgaEJRF/iQ0MT1z4C4xEQ7+Oxt2UEEEAAAQQQ2A8CnYkjQ2TUfPd3f3f6T//pP80SPz784Q+nBx98sHPGkDxDScb+6KOPpjbppP3Jm5e85CWz39z55je/mX7/938/PfHEE53trduvnDhixhEZhzIOZRyu6yM+xz/4B//gA3yAD4znA/GGsiePxaN47C8eY3zl6/f8Lt7Em3jrL96cT64WT9GX+NBq3OgLpyn6tXin2ynqlt/SLd06P+YDfGCXfKAzcSTf4Onr/WUve1l6y1veki699NL0hS98IX3wgx9cOivJ/DZf8YpXpJ/+6Z9Ojz/++OwzjzzyyNHiNnHklltumf10zec///n0x3/8x0fLuv7IiSBd68XlbcKLFwIIIIAAAggggAACCEyLwL/87B8s7PDBB355oayAAALrE4jxFVsSb5GIMgIIDE0g+hIfGpq49hEYj4B4H4+9LSOAAAIIIJAJtJNFeE2XQGfiSF+ZTi94wQvSv/t3/y792I/92Gx2kP/zf/5PapMvnn322bRKJs1FF12UvvM7v3OWOPKtb33reTOKtAkpv/ALvzBr63d+53dmP2mzSrsSR6YrXnuOAAIIIIAAAggggMBpCbihfFpi1kdgdQIxvuInfWEbiSgjgMDQBKIv8aGhiWsfgfEIiPfx2NsyAggggAACmcBnP/vZlb737yv/QDv9ztjSmTiSB3qT96uuuiq9+c1vTu1P1LRJHx//+MfTQw89tEmTxc++7W1vm/10zR/90R+lL37xi8V1Nq3MiSbvfve7N23K5xFAAAEEEEAAAQQQQGDLBOIU1v/7HT+65T2wOQT2l0CMr9hT8RaJKCOAwNAEoi/xoaGJax+B8QiI9/HY2zICCCCAAAII7AeBzsSRNlMnv45m8Ggq2tpcPmn5q171qvSGN7xhtu4DDzzkGW+ZAABAAElEQVSQPvGJT6Qn2xlDVvz8abZ/2223pauvvjp9/GMfS585f36l/TtN+21/c+LIu971rkHan2VGnYLvafdf+03mFb6DxF/2A3xX90fxu9nxBT/8Tjr/oA/6oI9DAo7PF34rd+78L95QfvDXXun8cI4P/+Sfm/hnjK/cVn5v4y2/+NPz/Un8iT/xcUigT384c+58xjp7nx33m/t7bby5f+H+Res6WW9ZKLlMH9PTx9kT4t34HhKgb/7v+Of47/g2veNb6+D8m39vxb+b66TOxJF8UrXO+6tf/er0H/7Df0hPPPFE+tCHPrTRLCCve93r0mWXXZY+/elPp8cee6y4O3nGkQ9+8IPpr//6r4vrbFqZE0fMOLIpSZ9HAAEEEEAAAQQQQGD7BOIX25483v4Y2OL+EojxFXsq3iIRZQQQGJpA9CU+NDRx7SMwHgHxPh57W0YAAQQQQACB/SDQmTgyy2DJmfineL/88svTHXfckZ555pn0P//n/0z/+I//eJwRdYp28vZvvvnmdN1116U/+7M/S//rf/2vwycD5tq58sor06/+6q/ORuW///f/nr7+9a9vtL283fieE0fmZxw5yvSa25/4OeV+f2MJTzzF3YUMS77zvOMBf+AP/IE/8AE+cJIPxBvKC08eO646rrZPoNPB2jqI8RVvm4g3+hJfztO2fZ4WfYkP8SE+tL8+JN7Ft/je3/je9vmD7fETfsJPavWBzsSReKNn1fKb3/zmdPbs2fSpT30q3XPPPat+LLUJIK9//etn67c/bfN3f/d3s7/PnDmTbrzxxvTtb387vf/9709f+tKXjtpsZyJ561vfml760pemBx98MH34wx8+Wtb3HzlxxIwjfZPVHgIIIIAAAggggAACwxOIN5Q9eTw8c1uoh0CMr9hz8RaJKCOAwNAEoi/xoaGJax+B8QiI9/HY2zICCCCAAAII7AeBzsSRdTNq8s/G/L//9//S008/vZRWu6ydkSRv5+Uvf3m67bbbZut/9KMfTffdd9/RE1+33HLLbNaRdt2HH344Pfroo7Ofr7n22mvTxRdfPCu/733vS08++eRRe7ndvt5z4ogZR2QcyjiUcdiXr2iHn/ATfsIH+AAf2J4PxBvKnjwWf+Kvv/iL8RVvBIg38Sbe+os354+rxVP0JT60Gjf6wmmKfi3e6XaKuuW3dEu3zo/5AB/YJR/oTByJN3pWKX/Hd3xH+rVf+7VVVk1PPfVU+q3f+q2jda+++up06623zspt4sj9999/tKzd2RtuuCG99rWvTZdeeulR/eOPPz6baaSd2eTZZ589qh/ij5w4YsaRIehqEwEEEEAAAQQQQACBYQnEG8qePB6Wt9brIhDjK/ZevEUiygggMDSB6Et8aGji2kdgPALifTz2towAAggggAAC+0GgM3FkVzOd2p+nufzyy9PXvva19MQTTww2w0jsf04cMeOIDLBdygCLOlWmT/qUqcwH+AAf4AN8oOwD8YayJ4/LnOgHl3WOIzG+4m0T8UZX6+iKH9HNJrqJvsSH6GkTPfGj3daPeN/t8RE/xof/uk/FB/gAH9h9H+hMHIk3emov58QRM47UrgT9RwABBBBAAAEEEJgigXhD2ZPHUxxF+7yrBGJ8xf0Ub5GIMgIIDE0g+hIfGpq49hEYj4B4H4+9LSOAAAIIIIDAfhDoTBxpM8Dy6ygTqKloa3O5puU5cWR+xpGa+j/LCKx4/PW/yYg0/tX6H/3Tv/iv9/xH/Iv/fYr/eEN59uSx8xvnN40Gary+7dvfY3w1WBdebbzlV+a9T/7S8swv/bvwJFUDpNb7R33HF32tF19nzp3PYTl7X5hxhD7FZ6OB7NdZKLns+DQ9/z57Qrwb30MC9O38xPmJ+zuOb9M7vrUOzr/591b8++AgdSaO5JMq74cEcuKIGUcoAgEEEEAAAQQQQACB6RGIX2x78nh6Y2iPd5dAjK+4p+ItElFGAIGhCURf4kNDE9c+AuMREO/jsbdlBBBAAAEEENgPAp2JI7MMlibDxHuTidhwyIkj8zOOHGV64UQnzRNm9MAv+KU44AN8gA/wAT6wuz4QbygvPHnsfN75vPP5ja5nYnzF2ybizfHR8XF3j4/7ev4afYkP8SE+tL8+JN7Ft/je3/je1/MU/eJbfItv7ZoPdCaOxBs9tZdz4ogZR2pXgv4jgAACCCCAAAIITJFAvKHsyeMpjqJ93lUCMb7ifoq3SEQZAQSGJhB9iQ8NTVz7CIxHQLyPx96WEUAAAQQQQGA/CHQmjuxapsvY+5MTR8w4IhNQJqBMwLH9yPb5EB/iQ3yAD/CB0/tAvKHsyWNxJI5OH0fLjj8xvuJtE/Em3sRbf/G2LA7VL8ZZ9CU+tMiHXvDYJ18W7/S8T3rmz/RMz86b+QAfGMMHOhNH4o2e2ss5ccSMI9NQQrxg8GTJNMbNXiKwiwTW9ZN1P7eLDOzT6gSM++qs9mnN2sZ9qv0der9XbX/V9fYpRvRl/wlEXcceux6LRE5XnirfuN90cLpxH3rtfR+f2L8unsv0GdtZd71V2+naz1WXL9vesvpV2611Pdx2e+SNT7/jg2e/PGtvbWg9Dd1+7eM39f7TR78jiGe/PHettc7EERlNixlNOXHEjCOLXHZVJ9HAPFkyjXHbVT3Zr7r1s66frPs5epu23oz7tMdv3firbdyn2t+h93vV9lddb109+lydPjT2uEddxxsgrsc20+VU+cb9poPNdNB3nO/7+MT+RV+K5WX6jO2su96q7fQ1zsu2t6y+r+3uazu47ZZ/RZ0Zn37HB89+eUa91lYeWk9Dt1/beO1bf+mjXz/Ds1+euxZvnYkj8QKq9nJOHDHjyDSUEA1s2RMh0+iNvUQAgTEJrOsn635uzL7a9uYEjPvmDKfYQm3jPtX+Dr3fq7a/6npTjAX7XC+BqOtIwvVYJHK68lT5xv2mg9ON+9Br7/v4xP518Vymz9jOuuut2k7Xfq66fNn2ltWv2m6t6+G22yNvfPodHzz75Vl7a0Praej2ax+/qfefPvodQTz75blrrXUmjrSZLvl19Fs6TUVbm8s1Lc+JI/MzjtTU/1nm04TGPxrY7ImQCe2/+OM//OWQQD7eHIwYvyU/WWV8zp47n1ebvc8/mZYX7EL/pubvu+6PJb2MqV/j22SCNwE39PnrmRPifRvbH7p/sf2p+tvQ8blq+1Plt+v+a//GPX+O+s/nOvm9PQ/KL+c/F36zuwES/bVlVOIzVb61HR+Xjd+u+tO+j0/sXzs+J73mr9fmz99i/C27v9R1fO9a3u5bKf7X1U/sf+5fqT+ZS5/bX9Xf1u3fttvf9vhtu39962/b+298tuMfbbzO++NU4nfq+p76/g8dn0O3P3X+te8/fTg+OL9d8f7DwUHqTBxpDcXrmEBOHDHjyDGTXf4rXggveyJkl/tg3xBAYDcIrOsn635uN3ptL9YlYNzXJTftz9U27lPt79D7vWr7q6437aiw97URiLqO/Xc9FomcrjxVvnG/6eB04z702vs+PrF/XTyX6TO2s+56q7bTtZ+rLl+2vWX1q7Zb63q47fbIG59+xwfPfnnW3trQehq6/drHb+r9p49+RxDPfnnuWmudiSOzDNImw8T74W8W5cSR+RlHjjKVcNo5nUQDy09W0PN+/waX8TW+Q/jyun6y7ufoeNo6Nu7THr9146+2cZ9qf4fe71XbX3W9dfXoc3X60NjjHnUdb4C4HttMl1PlG/ebDjbTQd9xvu/jE/sXfSmWl+kztrPuequ209c4L9vesvq+truv7eC2W/4VdWZ8+h0fPPvlGfVaW3loPQ3dfm3jtW/9pY9+/QzPfnnuWrx1Jo7EC6jayzlxxIwj01BCNLBlT4RMozf2EgEExiSwrp+s+7kx+2rbmxMw7psznGILtY37VPs79H6v2v6q600xFuxzvQSiriMJ12ORyOnKU+Ub95sOTjfuQ6+97+MT+9fFc5k+YzvrrrdqO137ueryZdtbVr9qu7Wuh9tuj7zx6Xd88OyXZ+2tDa2noduvffym3n/66HcE8eyX56611pk4smuZLmPvT04cMePINDKqooEteyJkbF3Z/jT0ZJzqHqd1/WTdz9HbtPVm3Kc9fuvGX23jPtX+Dr3fq7a/6nrr6tHn6vShscc96jreAHE9tpkup8o37jcdbKaDvuN838cn9i/6Uiwv02dsZ931Vm2nr3Fetr1l9X1td1/bwW23/CvqzPj0Oz549ssz6rW28tB6Grr92sZr3/pLH/36GZ798ty1eOtMHIkXULWXc+KIGUemoYRoYMueCJlGb+wlAgiMSWBdP1n3c2P21bY3J2DcN2c4xRZqG/ep9nfo/V61/VXXm2Is2Od6CURdRxKuxyKR05WnyjfuNx2cbtyHXnvfxyf2r4vnMn3GdtZdb9V2uvZz1eXLtresftV2a10Pt90eeePT7/jg2S/P2lsbWk9Dt1/7+E29//TR7wji2S/PXWutM3Fk1zJdxt6fnDhixpFpZFRFA1v2RMjYurL9aejJONU9Tuv6ybqfo7dp6824T3v81o2/2sZ9qv0der9XbX/V9dbVo8/V6UNjj3vUdbwB4npsM11OlW/cbzrYTAd9x/m+j0/sX/SlWF6mz9jOuuut2k5f47xse8vq+9ruvraD2275V9SZ8el3fPDsl2fUa23lofU0dPu1jde+9Zc++vUzPPvluWvx1pk4Ei+gai/nxBEzjkxDCdHAlj0RMo3e2EsEEBiTwLp+su7nxuyrbW9OwLhvznCKLdQ27lPt79D7vWr7q643xViwz/USiLqOJFyPRSKnK0+Vb9xvOjjduA+99r6PT+xfF89l+oztrLvequ107eeqy5dtb1n9qu3Wuh5uuz3yxqff8cGzX561tza0noZuv/bxm3r/6aPfEcSzX5671lpn4kib6ZJfBwcHaZb50lS0tblc0/KcODI/40hN/Z/a+EcDmz0RUrF+pzZ+/If/7pK/lvxklf07e+58Xm32Pv9kWl6Qj6cHTUWtx9d986eSXozv/uv7zAnxvo/jP1V/Gzo+V21/qvycnzk/O+n8Jeo/r5vf2/Og/HL+c/r7K1PlW9vxsdX4lPS97+MT+5c9aNn7/PXa/PlbjL9l95e6ju9dy/vWT+x/7l+pP5nJlPS77evnbY/ftvvXt/62vf/Gp9/jzzL/2Lf7N65vtnN9M3R8Dt3+1P2x9v2nD8cH57cr3n9o8kA6E0fyRYP3QwI5ccSMI9NQRLwQXvZEyDR6Yy8RQGBMAuv6ybqfG7Ovtr05AeO+OcMptlDbuE+1v0Pv96rtr7reFGPBPtdLIOo6knA9FomcrjxVvnG/6eB04z702vs+PrF/XTyX6TO2s+56q7bTtZ+rLl+2vWX1q7Zb63q47fbIG59+xwfPfnnW3trQehq6/drHb+r9p49+RxDPfnnuWmudiSOzDNI804j3lBNH5mccOcpUwudwRpod4hANLD9ZQdf7/Rtcxtf4DuHL6/rJup+j42nr2LhPe/zWjb/axn2q/R16v1dtf9X11tWjz9XpQ2OPe9R1vAHiemwzXU6Vb9xvOthMB33H+b6PT+xf9KVYXqbP2M66663aTl/jvGx7y+r72u6+toPbbvlX1Jnx6Xd88OyXZ9RrbeWh9TR0+7WN1771lz769TM8++W5a/HWmTgSL6BqL+fEETOOTEMJ0cCWPREyjd7YSwQQGJPAun6y7ufG7Kttb07AuG/OcIot1DbuU+3v0Pu9avurrjfFWLDP9RKIuo4kXI9FIqcrT5Vv3G86ON24D732vo9P7F8Xz2X6jO2su96q7XTt56rLl21vWf2q7da6Hm67PfLGp9/xwbNfnrW3NrSehm6/9vGbev/po98RxLNfnrvWWmfiyK5luoy9PzlxxIwj08ioiga27ImQsXVl+9PQk3Gqe5zW9ZN1P0dv09abcZ/2+K0bf7WN+1T7O/R+r9r+quutq0efq9OHxh73qOt4A8T12Ga6nCrfuN90sJkO+o7zfR+f2L/oS7G8TJ+xnXXXW7WdvsZ52faW1fe13X1tB7fd8q+oM+PT7/jg2S/PqNfaykPraej2axuvfesvffTrZ3j2y3PX4q0zcSReQNVezokjZhyZhhKigS17ImQavbGXCCAwJoF1/WTdz43ZV9venIBx35zhFFuobdyn2t+h93vV9lddb4qxYJ/rJRB1HUm4HotETleeKt+433RwunEfeu19H5/Yvy6ey/QZ21l3vVXb6drPVZcv296y+lXbrXU93HZ75I1Pv+ODZ788a29taD0N3X7t4zf1/tNHvyOIZ788d621zsSRXct0GXt/cuKIGUemkVEVDWzZEyFj68r2p6En41T3OK3rJ+t+jt6mrTfjPu3xWzf+ahv3qfZ36P1etf1V11tXjz5Xpw+NPe5R1/EGiOuxzXQ5Vb5xv+lgMx30Hef7Pj6xf9GXYnmZPmM76663ajt9jfOy7S2r72u7+9oObrvlX1Fnxqff8cGzX55Rr7WVh9bT0O3XNl771l/66NfP8OyX567FW2fiSLyAqr2cE0fMODINJUQDW/ZEyDR6Yy8RQGBMAuv6ybqfG7Ovtr05AeO+OcMptlDbuE+1v0Pv96rtr7reFGPBPtdLIOo6knA9FomcrjxVvnG/6eB04z702vs+PrF/XTyX6TO2s+56q7bTtZ+rLl+2vWX1q7Zb63q47fbIG59+xwfPfnnW3trQehq6/drHb+r9p49+RxDPfnnuWmudiSNtpkt+HRwcpFnmS1PR1uZyTctz4sj8jCM19X9q4x8NbPZESMX6ndr48R/+u0v+WvKTVfbv7LnzebXZ+/yTaXlBPp4eNBW1Hl/3zZ9KejG++6/vMyfE+z6O/1T9bej4XLX9qfJzfub87KTzl6j/vG5+b8+D8sv5z+nvr0yVb23Hx1bjU9L3vo9P7F/2oGXv89dr8+dvMf6W3V/qOr53Le9bP7H/uX+l/mQmU9Lvtq+ftz1+2+5f3/rb9v4bn36PP8v8Y9/u37i+2c71zdDxOXT7U/fH2vefPhwfnN+ueP+hyQPpTBzJFw3eDwnkxBEzjkxDEfFCeNkTIdPojb1EAIExCazrJ+t+bsy+2vbmBIz75gyn2EJt4z7V/g6936u2v+p6U4wF+1wvgajrSML1WCRyuvJU+cb9poPTjfvQa+/7+MT+dfFcps/YzrrrrdpO136uunzZ9pbVr9purevhttsjb3z6HR88++VZe2tD62no9msfv6n3nz76HUE8++W5a611Jo7MMkjzTCPeU04cmZ9x5ChTCZ/DGWl2iEM0sPxkBV3v929wGV/jO4Qvr+sn636OjqetY+M+7fFbN/5qG/ep9nfo/V61/VXXW1ePPlenD4097lHX8QaI67HNdDlVvnG/6WAzHfQd5/s+PrF/0ZdieZk+YzvrrrdqO32N87LtLavva7v72g5uu+VfUWfGp9/xwbNfnlGvtZWH1tPQ7dc2XvvWX/ro18/w7JfnrsVbZ+JIvICqvZwTR8w4Mg0lRANb9kTINHpjLxFAYEwC6/rJup8bs6+2vTkB4745wym2UNu4T7W/Q+/3qu2vut4UY8E+10sg6jqScD0WiZyuPFW+cb/p4HTjPvTa+z4+sX9dPJfpM7az7nqrttO1n6suX7a9ZfWrtlvrerjt9sgbn37HB89+edbe2tB6Grr92sdv6v2nj35HEM9+ee5aa52JI7uW6TL2/uTEETOOTCOjKhrYsidCxtaV7U9DT8ap7nFa10/W/Ry9TVtvxn3a47du/NU27lPt79D7vWr7q663rh59rk4fGnvco67jDRDXY5vpcqp8437TwWY66DvO9318Yv+iL8XyMn3GdtZdb9V2+hrnZdtbVt/Xdve1Hdx2y7+izoxPv+ODZ788o15rKw+tp6Hbr2289q2/9NGvn+HZL89di7fOxJF4AVV7OSeOmHFkGkqIBrbsiZBp9MZeIoDAmATW9ZN1PzdmX217cwLGfXOGU2yhtnGfan+H3u9V2191vSnGgn2ul0DUdSTheiwSOV15qnzjftPB6cZ96LX3fXxi/7p4LtNnbGfd9VZtp2s/V12+bHvL6ldtt9b1cNvtkTc+/Y4Pnv3yrL21ofU0dPu1j9/U+08f/Y4gnv3y3LXWOhNHdi3TZez9yYkjZhyZRkZVNLBlT4SMrSvbn4aejFPd47Sun6z7OXqbtt6M+7THb934q23cp9rfofd71fZXXW9dPfpcnT409rhHXccbIK7HNtPlVPnG/aaDzXTQd5zv+/jE/kVfiuVl+oztrLvequ30Nc7Ltresvq/t7ms7uO2Wf0WdGZ9+xwfPfnlGvdZWHlpPQ7df23jtW3/po18/w7NfnrsWb52JI/ECqvZyThwx48g0lBANbNkTIdPojb1EAIExCazrJ+t+bsy+2vbmBIz75gyn2EJt4z7V/g6936u2v+p6U4wF+1wvgajrSML1WCRyuvJU+cb9poPTjfvQa+/7+MT+dfFcps/YzrrrrdpO136uunzZ9pbVr9purevhttsjb3z6HR88++VZe2tD62no9msfv6n3nz76HUE8++W5a611Jo60mS75dXBwkGaZL01FW5vLNS3PiSPzM47U1P+pjX80sNkTIRXrd2rjx3/47y75a8lPVtm/s+fO59Vm7/NPpuUF+Xh60FTUenzdN38q6cX47r++z5wQ7/s4/lP1t6Hjc9X2p8rP+Znzs5POX6L+87r5vT0Pyi/nP6e/vzJVvrUdH1uNT0nf+z4+sX/Zg5a9z1+vzZ+/xfhbdn+p6/jetbxv/cT+5/6V+pOZTEm/275+3vb4bbt/fetv2/tvfPo9/izzj327f+P6ZjvXN0PH59DtT90fa99/+nB8cH674v2HJg+kM3EkXzR4PySQE0fMODINRcQL4WVPhEyjN/YSAQTGJLCun6z7uTH7atubEzDumzOcYgu1jftU+zv0fq/a/qrrTTEW7HO9BKKuIwnXY5HI6cpT5Rv3mw5ON+5Dr73v4xP718VzmT5jO+uut2o7Xfu56vJl21tWv2q7ta6H226PvPHpd3zw7Jdn7a0Nraeh2699/Kbef/rodwTx7JfnrrXWmTgyyyDNM414TzlxZH7GkaNMJXwOZ6TZIQ7RwPKTFXS937/BZXyN7xC+vK6frPs5Op62jo37tMdv3firbdyn2t+h93vV9lddb109+lydPjT2uEddxxsgrsc20+VU+cb9poPNdNB3nO/7+MT+RV+K5WX6jO2su96q7fQ1zsu2t6y+r+3uazu47ZZ/RZ0Zn37HB89+eUa91lYeWk9Dt1/beO1bf+mjXz/Ds1+euxZvnYkj8QKq9nIMiPyEQazv4uRzZUK44FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnoZh0vmXt662l0n0Jk4smuZLmPvT0wQyU8YxPqugfe5MiFccCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb2MwyVzH/v7fNtfb2aUzsSRsqzqrY0JIjlzKtZ3EfK5MiFccCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb2MwyVzL29d7a4T6EwckZGzmJETE0Ry5lSs7xp4nysTwgWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSyzhcMnf5BYv5BVPh0Zk4UpZVvbUxQSRnTsX6LkI+VyaECy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqWXcbhk7uWtq911Ap2JI20GTH4dHBykWUZMU9HW5nJNy2OCSJs51b7OnjufMaz0njOuYntdH559rlnpzBa31+6T/i2OjPFb5JFL9JlJLL5vope2JfHXH8+Dpin+2R/PtiX67I8nfS6ybEv88/lMMpf2Xfwt8tlEL+JvkWVb2oRn+3n6bCkcvzbhSZ/HHPNfm/Bs26DPTPLwfROe9LnIsi1twrP9PH22FI5fm/Ckz2OO+a9NeLZt0Gcmefi+CU/6XGTZljbh2X6ePlsKx69NeNLnMcf81yY82zboM5M8fN+EJ30usmxLm/BsP0+fLYXj1yY86fNYj/ILJphf0eSBdCaOHIeKv1oCMdEjZ07F+i5aPlcmhAsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6ll3G4ZO7lravddQKdiSNT+c2dbe1nTBDZKPOsydyJ7XUJxvbKhHDBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19LIZl8xvW9/b286/HP1CTP6lmE3eOxNHyvKotzYmeuTMqVjfRcjnyoRwwaVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfQyDpfMvbx1tbtOoDNxRKbOYqZOTBDJmVOxvmvgfa5MCBdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL+NwydzlFyzmF0yFR2fiSFlW9dbGBJGcORXruwj5XJkQLriUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZexuGSuZe3rnbXCXQmjkwlA2Zb+xkTRHLmVKzvGnifKxPCBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19LLOFwy9219b287/c5s0pk4UpZVvbUxQSRnTsX6LkI+VyaECy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqWXcbhk7uWtq911Ap2JI22mTn4dHBykWeZOU9HW5nJNy2OCSJs51b7OnjufMaz0njOuYntdH559rlnpzBa31+6T/i2OjPFb5JFL9JlJLL5vope2JfHXH8+Dpin+2R/PtiX67I8nfS6ybEv88/lMMpf2Xfwt8tlEL+JvkWVb2oRn+3n6bCkcvzbhSZ/HHPNfm/Bs26DPTPLwfROe9LnIsi1twrP9PH22FI5fm/Ckz2OO+a9NeLZt0Gcmefi+CU/6XGTZljbh2X6ePlsKx69NeNLnMcf81yY82zboM5M8fN+EJ30usmxLm/BsP0+fLYXj1yY86fNYj/ILJphf0eSBdCaOHIeKv1oCMdEjZ07F+i5aPlcmhAsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6ll3G4ZO7lravddQKdiSN+G2jxt4FigshGmWdN5k5sr0swtlcmhAsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6ll824ZH7yCxbzC6bCozNxpCyPemtjokfOnIr1XYR8rkwIF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsv43DJ3MtbV7vrBDoTR6aSAbOt/YwJIjlzKtZ3DbzPlQnhgkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+llHC6Z+7a+t7edfmc26UwcKcuq3tqYIJIzp2J9FyGfKxPCBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19LLOFwy9/LW1e46gc7EEZk6i5k6MUEkZ07F+q6B97kyIVxwKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItvYzDJXOXX7CYXzAVHp2JI2VZ1VsbE0Ry5lSs7yLkc2VCuOBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6GYdL5l7eutpdJ9CZONJmwOTXwcFBmmXENBVtbS7XtDwmiLSZU+3r7LnzGcNK7znjKrbX9eHZ55qVzmxxe+0+6d/iyBi/RR65RJ+ZxOL7JnppWxJ//fE8aJrin/3xbFuiz/540uciy7bEP5/PJHNp38XfIp9N9CL+Flm2pU14tp+nz5bC8WsTnvR5zDH/tQnPtg36zCQP3zfhSZ+LLNvSJjzbz9NnS+H4tQlP+jzmmP/ahGfbBn1mkofvm/Ckz0WWbWkTnu3n6bOlcPzahCd9HnPMf23Cs22DPjPJw/dNeNLnIsu2tAnP9vP02VI4fm3Ckz6P9Si/YIL5FU0eSGfiyHGo7MZfL3zhC9NrXvOadO2116Yrr7wyPfvss+mrX/1q+qu/+qt03333zRJbhtzTmOiRM6difdc++FyZEC64lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXsbhkrmXt6521wl0Jo7s0m/uXHLJJen2229PV1xxxYzrY489li666KLU1revhx9+OL3//e+fJY8Mtd8xQWSjzLMmcye2N+vICf/ZXhkOLriUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZeNuOS+Q31Pb12/+XoF2HyL8P0+d6ZOFKWxzi1N910U7r++uvT17/+9XTnnXfOZhpp9+Saa65JN998c7r44ovT3Xffne69997BdjAmeuTMqVjftQM+VyaECy5lAuVaesGlTKBcSy+4lAmUa+kFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqWXcbhk7uWtq911Ap2JI7uSudPOKvL2t799lkXz3ve+N33pS19ayKg5c+ZMuvHGG9MTTzyRfvu3fzs999xzC8v76kdMEMmZU7G+a+B9rkwIF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsv43DJ3Pv6Xl47w84wEvl2Jo6UZbX92pwY8pWvfCW95z3ved4OtB359V//9dlP17SJJY888sjz1umjIiaI5MypWN+1LZ8rE8IFlzKBci294FImUK6lF1zKBMq19IJLmUC5ll5wKRMo19ILLmUC5Vp6waVMoFxLL7iUCZRr6QWXMoFyLb3gUiZQrqUXXMoEyrX0gkuZQLmWXnApEyjX0ss4XDL38tbV7jqBzsSRmGkyVvkNb3hD+vEf//H0wAMPpLvuuiuV9uP2229PV111VfrIRz4yW6/P3/TJ24sJIjlzKtZ3DbzPlQnhgkuZQLmWXnApEyjX0gsuZQLlWnrBpUygXEsvuJQJlGvpBZcygXItveBSJlCupRdcygTKtfSCS5lAuZZecCkTKNfSCy5lAuVaesGlTKBcSy+4lAmUa+llHC6Ze/5e3ft2ZwzZlHdn4khZVtuvvfnmm9N1112X7rnnnvSpT32quAM/9VM/lX74h394trxdb5XXO9/5zlVWsw4CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIFAi8+93vLtSqmgqBzsSRNjMlv45m8Ggq2tpc3sbyn/u5n0s/8AM/kP7kT/4kfeYv/7K4/Te96U3phhtuSH/xF3+R/vRP/3Sl/ZM4kkfPOwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqcn8K53vWul7+d3Jf+g7WHOdzho/t52/sNObf/gIHUmjrQ7vAuvW2+9NV199dXp4x//eDp//nxxl37iJ34ivfKVr5wtb9fbxisnnsQMqljfVc772rVeXO5zh5lruJQ54IJL9oj2Peqhq5w/27VeXO5zZd3hgkurgXy+EOMmlumFXujlOF7Eg3gQD+IhHidjmU/wCT7BJ6IvxDKf4BN8gk9EX4hlPsEn+ASfiL4Qy3yCT/CJbp/IceJ9mgQ6E0c2/S2cvj7fzjhy7bXXzmYS+ct2xpFmJpSjDKAmA6Yt/+RP/mT6kR/5kdQub2cmicuHKOcDx3wGVbudWN9Vzv3pWi8u97nDzDVcyhxwwWXe96Ieusr8pawfXHBpT/nycV8cHZ6PdXGIy8WROBJHxz4iHsSDeBAP8TgZy3yCT/AJPhF9IZb5BJ/gE3wi+kIs8wk+wSf4RPSFWOYTfKIPn8g68v78PIb57+t2lU9n4kgrkl14/czP/Ez6wR/8wXT33Xene++9t7hLN910U7r++uvTpz/96fTJT36yuE7fldlY8xPEuf1Y31X2OZmKrQayjujlMCK6OMTl4kgciaNjHxEP4kE8iId4nIxlPsEn+ASfiL4Qy3yCT/AJPhF9IZb5BJ/gE3wi+kIs8wk+wSf4RPSFWOYTfIJP7I9P5Hj2Pk0CnYkju5Lxkn+G5oEHHkh33XVXccaR22+/PV111VWz2UbaWUe2kbmTD3D5yePMK9Z3lX1OJl9rIVlH9OIJ9tLMRXyCT/CJY58UD+JBPIgH50vOl5wvHfuAeBAP4kE85PsJzpOdJztPPvYD8SAexIN4cJ7sPNl58rEPiAfxsI14yOdf3g/1NjUOnYkj7cnVLrxe9apXpTe+8Y3pkUceSe9973uLu/SOd7wjXXzxxekDH/hA+tu//dviOn1XZqPNM0Xk9mN9V9nnZFS2Gsg6opfDiOjiEJeLI3Ekjo59RDyIB/EgHuJxMpb5BJ/gE3wi+kIs8wk+wSf4RPSFWOYTfIJP8InoC7HMJ/gEn+AT0RdimU/wCT6xPz6R49n7NAl0Jo7sSibMFVdcke6444703HPPpT/4gz9IX/nKVxZmFPm3//bfpre+9a3pmWeeSefOnUtPP/30wvKh+pEPcJ7seOcsAjIHXMoZ/bjg0mZ0Zj+Keugq+1xZP7jg0h6Alh1/YlzRC73Qy3G8iAfxIB7EQzxOxjKf4BN8gk9EX4hlPsEn+ASfiL4Qy3yCT/AJPhF9IZb5BJ/gE/vjEzmevZtxpI3rQV+/9Eu/lF760pemv//7v0933nlneuqpp2bbu/TSS9Mv/uIvpssvvzx97nOfSx/60IcG3Y/5xvMBLs8UkZfF+q6yz8mobDWQdUQvhxHRxSEuF0fiSBwd+4h4EA/iQTzE42Qs8wk+wSf4RPSFWOYTfIJP8InoC7HMJ/gEn+AT0RdimU/wCT7BJ6IvxDKf4BN8Yn98Isez92kSWGnGkdy1oyflm4p/af7l8raWv/jFL54liLzwhS+czSzyhS98Ib3oRS9K11xzTWo78sQTT6Tf+73fS99q3re1f/kAl590nmVQNUDe+V/+ywxLW9++/stc+aApx+XtOqXflortx3K7vfbV1X5cnvnE9rrK+tdkfja8jV85Azbqhz7L8S/+yvrhL/ylPZ7l42n0k1jmL/zltHopHb/j+ZHje9mfxV/Zn6N+HN/L+nF8L+un9TDXfweJv5T1wV/KflLSi+O7+xOuHw7jxfWR6yPXR8f3E5x/ls8vnH+Wzy/4J//kn/wzXn+5P1j2y9L1WBs/kV++P5b9NR9/8vHZ8u3nV+R8jvb6+UT+zS8mdCaOtAO6S68rr7wyvelNb0ove9nLZjfa2n1rf77mb/7mb9LHPvax9M///M9b3d0cKHmmiLzxWN9V9jkZla0Gso7o5TAiujjE5eJIHImjYx8RD+JBPIiHeJyMZT7BJ/gEn4i+EMt8gk/wCT4RfSGW+QSf4BN8IvpCLPMJPsEn+ET0hVjmE3yCT+yPT+R49j5NAp2JI7MMoCbDZNfeX/CCF6Q2ieSZZ55J//RP/zR7P8qY2eL+5gNcfsIhc4r1XWWfK2ew4YJLa605vsTR4W+idXGIy8WROBJHxz4iHsSDeBAP8TgZy3yCT/AJPhF9IZb5BJ/gE3wi+kIs8wk+wSf4RPSFWOYTfIJP8InoC7HMJ/jEFH0i69b74fd5U+PQmTjSitJrOYFs5HmmiLxmrO8q+5yMylYDWUf0chgRXRzicnEkjsTRsY+IB/EgHsRDPE7GMp/gE3yCT0RfiGU+wSf4BJ+IvhDLfIJP8Ak+EX0hlvkEn+ATfCL6QizzCT7BJ/bHJ3I8e58mgc7Ekallwmx7f/MBLs+IkLcf67vKPidzsLWQrCN6MbNG6Tfv+QSf4BPHPikexIN4EA/Ol5wvOV869gHxIB7Eg3jI9xOcJztPdp587AfiQTyIB/HgPNl5svPkYx8QD+JhG/GQz7+8H+ptahw6E0fakysvBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgf0j0Jk4MrVMGPs7zQwm42bc2kxHOqADOhAHfIAP8AE+wAf4AB/gA3yAD/ABPsAH+AAf4AN8gA/wAT7AB/gAH+ADfGC7PtCZOLJ/uTJ6hAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINAS6EwcaTN58usos6upaGtz2fJDApnHAT70IT74w4UZVPgjf2wJOD5cyIxuWDh/ONbDYXQcl50/0If4OI4H8XFIwPHD8WP2ZI3jp/OHRgPZD/gjf2wJZD04f3T+6PzxOB74I3/kj8fx4Pjg+OD4cBwPjg+OD44Px/Hg+OD4cOLxofleszNxJJuqdwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH9ItCZOOK3g7b720F443305NCFGSuU/YYbX+ALfIAP8AE+wAf4AB/gA3yAD/ABPsAH+AAf4AN8gA/wAT7AB/gAH+ADfIAPDOUDnYkj+5UnozcIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACmUBn4shQGSvalREnI05GHB/gA3yAD/ABPsAH+AAf4AN8gA/wAT7AB/gAH+ADfIAP8AE+wAf4AB/gA3yAD/CBcX2gM3EkZ5h4RwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEENgvAp2JIzJ7xs3swR9/GZYyLPkAH+ADfIAP8AE+wAf4AB/gA3yAD/ABPsAH+AAf4AN8gA/wAT7AB/gAH+ADfGAoH+hMHNmvPBm9QQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMgEOhNH2oyV/DrKYGoq2tpctvyQQOZxgA99iA/+cHCY8cgf+WNLwPHhQgZww8L5w7EeDqPjuOz8gT7Ex3E8iI9DAo4fjh+zJ0gcP50/NBrIfsAf+WNLIOvB+aPzR+ePx/HAH/kjfzyOB8cHxwfHh+N4cHxwfHB8OI4HxwfHhxOPD833mp2JI9lUvSOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjsF4HOxJGhfiNHu36D6+gJmQszMyj7TS6+wBf4AB/gA3yAD/ABPsAH+AAf4AN8gA/wAT7AB/gAH+ADfIAP8AE+wAf4AB/gA9v1gc7Ekf3Kk9EbBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUygM3FEJs92M3nwxlsGpQxKPsAH+AAf4AN8gA/wAT7AB/gAH+ADfIAP8AE+wAf4AB/gA3yAD/ABPsAH+AAf2JYPdCaO5AwT7wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAL7RaAzcWRbGSy2I2NOxpyMOT7AB/gAH+ADfIAP8AE+wAf4AB/gA3yAD/ABPsAH+AAf4AN8gA/wAT7AB/gAH+AD2/WBzsSR/cqT0RsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBTKAzcaTN5Mmvo8yupqKtzWXLDwlkHgen5HPllVem7//+70+XXHJJ+od/+If0D1/+cnrq6acnyfe7v/u703U/9EPppd/3fanVzpebvnzuc59L3/jGN476c1o+u6CvdmyuuOKK9NWvfjU99eST9N8MStb7LoxPuw95f6aor33a/+9p/Oz75vys9YCn5/xsauOzT/7cevK+jM/3fM/3pO9rjjNHx83m2PnUU0+lqekr++ey/uTlU/O3Zf2Z2viI/0MF7qr+9mV8lsXL/2fvPbytqJL24e3nDEmigCBKRgliAhVRUBAUR3GNBFEQHIIyKM4sdJa8+he41qi/GWd43yGMJAEJOsoQVXIwEMRRQTIDkpEgoiTBr5+61rl9+3Y6ffqc3l1377Xu3afzfuqpqu5zdnWVsX899E8KP1Lu/+yP0mofZvxFb8oZ/vTwb6yPaXs+M/pj9AcSMPr7y5vHlizM7/PF+lBkHcXLxr8Z/TD2UWwPxj6KJGDuH+b+QZkrzP3TPD9YOsD+wPhHh3+85BIVGDjCQjN9/BLApFfXrl1Vw4YNS5z81KlTavbs2WrHjh0l1uu8ULFiRdWlSxd10003kVLZx3rGCrSYN2+e2rhxo3219p/hOK6//nrVoUMHChrhAR86dEjNnTuXgmJ4XZp6TLTWrFlTHTlyRIGbtDeDRw8GJfkzSNTg0UOvnKMwvDgloteyFH6k4GDtMHhYEnr1hhe9+HCORgo/UnDY+cH3mCuvvFJ99dVX9tWp/SwFjxQcrEgGD0tCr97wohcfztFI4UcKDubH4GFJ6NUbXvTiwzkaKfxIwcH8GDwsCb16w4tefDhHI40fJz6znB8JBAaOUASWNYFu+vhqCEHod999t+rYsSNFNYFayPenn35Sv/71r4lpLC9fvpz+OPJJ1x4/SD7yyCOqWrVqGS1FlgEEJSBLB69fsmSJWrlyZSaSS1c8GFf16tUJE36UdGsXL15Uy5YtSw0eYLjhhhs8g2D27t2bCl7YDxk88fmjXO1Qmj8zePS83xte9OSF/YcUfqTgkMaLNDxGz4w/4+fZfPbS9Iz9QPny5dXw4cNVlSpV1OrVq9WKFStKZLbDd7gTJ06k5nuNFDxScBg909s/Gz0z/OTzvmnsX2/9ksaP8Wd665sUfqTgkGb/0vAYPTP+rBDPZ2w3pi+cvuE3Jcgb7ZKWLVtaPCOJV1EgAybHTYtXAggM6d27t7rmmmvoxAgWwY9eGzZsoHIunLnj5ptvpu3Tpk3TOvNI8+bNVa9evdSvfvUrGu/WrVvV+++/r44fP07LWI8gmTvvvFNBnyZMmKB9po6mTZsSpgoVKhCGY8eOqXXr1qk9e/Yo3AzbtWunrr32Wtr2zjvvaJ9JpUaNGqRzQUEwq1atIky6/zN49GFImj8zePS83xhe9OSFPZEUfqTgkMaLNDxGz4w/Y53OZy9Nz5yywvfk7t27048YP/74o9q0aZM6ffo0ZSHB97i1a9eqhQsXOg/TdlkKHik4WFEMHpaEXr3hRS8+nKORwo8UHMyPwcOS0Ks3vOjFh3M0UviRgoP5MXhYEnr1hhe9+HCORho/TnxmOT8SCAwcMRFD8b3Zj0CEvn37qgYNGhCbKHkyc+ZMCrJwyhk/hrVp00ahbM2oUaPU2bNntXtzqnXr1qpHjx5UmgZBIfiBDj/UuUV+PfbYYxRscfjwYTV69GhtM9i0aNGCgkYuvfRSGuNHH31EmUUQ4GPHdc8991D2DmRVAZ7vvvuuxHYnn0ktN2nShIJGnEEwu3fvVljnDIJBymc7zqTG7XVdg0cffqT5M4NHz/uN4UVPXvg+IYUfKTik8SINj9Ez48+8nm/jXC9Nz9gPOHt8T+bgEefPJAj4X7BgAb204DxO12UpeKTgYD0xeAr3Rls2ftDwoicv0uzG6JnRs2z8Eut/tr3RM6NnRs+yn3czdqOn3Rhe9OSF70vS+GFcps+f3gUGjjh/iDHL0SSAN7/69++v6tevTyfYsWOHmjVrFqXWdTsjfvQbOXIkbZoyZYrauXOn226JrUOABTKnQIEQVIEAmO3bt3uOp1GjRuqJJ56g7a+88gq9Fea5c0IbWrVqpXr27JnBNHv2bM9sIggsefbZZ6kMz+LFiylrTELD9ryslUGI8DiDYC5cuFDimC5dulBGGA6COXnyZIntuiwYPEVBSjrwI82fGTx63m8ML3rywvcEKfxIwSGNF2l4jJ4Zf8Y6nc9emp4Fycr+5hTvu379ejV//nx6AYDXpaWXgkcKDtYbg4cloVdveNGLD+dopPAjBQfzY/CwJPTqDS968eEcjRR+pOBgfgweloReveFFLz6co5HGjxOfWY5XAoGBI4WIfJQeGYSJ+379+ilkS0Dbtm0bBVpgAt9Pvi+99JIqV66cQmACyojoIqeGDRuqAQMGKOBC0Mj06dOpnI7f+OrWrauGDh1K+CdOnKiQ8cJvfz+55OO4Zs2aqUcffTSD6a233lK7du3y5efxxx9XOO7rr78mPvMxrqhyQJAFSghxYM97771HaZzdzodyQhwEs2jRIoUsK277JYnP4CkKUtKBH2n+zOApikzV7X5jeNGTF74PSOFHCg5pvEjDY/TM+LNCPFdL0zP2A349MOP7Ze3atek7ZhozjdjxScEjBQfbrcGT/ZvAdr1mOcbdG1705IV5lsKPFBzSeJGGx+iZ8Wfmvpn9G/PGbvS0G8OLnrxIu28yHtPnV98CA0foVxjzLycJdOvWjUqC4CQIRpg2bZpyZn1wXqBKlSpqxIgRFFwxZ84ctWHDBucuiSxXrlyZfqBDj/I0yDSydevWwLGgJArkgPbaa6+pH374IfCYQu1Qn7/kEgAAQABJREFUq1Yt9eSTT1KQDnjhQJig6w8aNIgyyGzZskXNmDEjaPeCbb/mmmtUnz59MkEwwBOUsQaBTRwEg0w4OjWDR1HgmS78SPJn0HODRykd7zeGFz154XuDFH6k4JDGizQ8Rs+MP2OdzmcvTc+CZIXAd5RC5Rcz0pxpBFil4JGCg/XP4GFJ6NUbXvTiwzkaKfxIwcH8GDwsCb16w4tefDhHI4UfKTiYH4OHJaFXb3jRiw/naKTx48RnluOXQGDgCCJ3uGUiIK0VWMvLZnuRBFgel9jkg0nvvn370g4HDx5U48ePVz+dPx8ov4533aU6d+5Mx/3jH/9Qhw8fzsjbfv5C84Mf6K699loa19y5cxV+pOPmhh/jg2MaNmyYqlmzpjp27Jga9fe/B+IvlH4hEvL3v/+9QvAIxoqgic1WBpGg61erXl398Y9/JE6WLFmiVq5cqQU/kPFTTz2VCYJB5hSURULz4gfbBg8eXBwEYwWaBOEv1HY3PDstPEHXH2TDg8AZND/8hdruhidN/Lj5s/OWPwuS3102fzba8meHbP6MDrb+JcGPG540+2c3PGnk5xrrHlPqvmllt4KP9rv/dezYMXPfHD16tDp06BCrVyL6xfdn3DO98PAA3fTfDY8f/iD5xLVdCj9u9mLs3/jnoOeLbLcb+y+6v7v5Mz//x/4T+7j5x7i2S+HHzZ+l8f4f1r7w/Q33VQ4aoUwjVnmai3hOuKTojUUd9Cus/rrh4XI7jEeH+38QP5da3/lL8bJgAb1skobxO/HhNwwvPGnSL37+k8KPm70sMPYf+PuIU7/D+ifWn7DHS+HH2H9+n79y1T8p/LjZSxrv/+wf3PAY/5z8/JmU+z9//5Ni/3x/lcKPsX94wqKm4/c3afyw/fD9h/0DGNBR/qkcn/W7SmDgyC86b7oIEoBwETCBoAT8eDd27Fh19OjRwDPVqFFDPf300xRwsWfPHoXSLmis+LSQwL8GDRqogQMH0pWzybJxzz33qA4dOtBxs2fPVv/5z3/oc8WKFdXp06fpc1L/7rzzTtWlSxe6PEq0oBRImIayNs2bN6cfwjBB+e2334Y5LK/74OEJ6Zo5CObtt9+mMjpBF61uBcH84Q9/IP1aunQpBcEEHVOI7QZPkZR14Sduf1YIHfK7hsFTJB2v+42f7PK5zfCiJy/MuRR+pOCQxos0PEbPjD9jnc5nL03PgmSF7wcm00iQlAq/3fBSeJlnc0Up/EjBwdwZPCwJvXrDi158OEcjhR8pOJgfg4cloVdveNGLD+dopPAjBQfzY/CwJExf1iWA35oQj4B2ScuWLa0AmKIIKfQoRYKeAxZMn13NNUueVDIEwl1gvW2zdu3aQHnCOSH7Q926dWnfCRMmqL1799Ln7t27qxUrVqjvv/8+8Dz54K1nz57q+uuvVz9Zb37/5S9/oaCPoOu0aNFCPfLII6RDyLiC4Bk04ESmD2RgQfBI0Hnysb1cuXLqueeeUxUqVFAY2xtvvEHYgvS8bdu2ClygoYQQSglhfOB73759ifGD4BwOglm9erVavHhxKLnix1cOgkF2GwQ35UPeQXJ1bjd4ivyNLvzE7c+cfBd62eC5hPyw1/2m0Hzw9QwvevIijR+jZ0bPCvGcY/TM6JnRs3h/R3B9U4ozWvySaYTvV2nopeCRgoPt1eCJ125Zrrn2hhc9eWFepfAjBYc0XqThMXpm/FkhnlONnhk9M3qW3Twu5GXsRm+74ecB0xeGp8DAEZodN/8iSeDee+9V7du3p0n4V199NTC7BpwTMlk0a9aMrrdq1SqFMijcXnjhBVq2l4fhbYXohw8fTuVmdu/erSZNmhR4ycaNG1N6VwSJnDlzRo0bN04dP36cjrv66qspQAYBKAiESaI1bNhQ/e53v6NL//vf/1aff/554DCaNm1KmGA4J0+epECYH3/8kY777W9/SxOx77zzTuB54t7h17/+dakgmAsXLgRepk2bNqWCYHAQAn72799PGANPkocdDJ4ioerET9z+DAhr166trrrqKlWpUiV14MABCrw6d+5cHjSq9CkNHv/7TVL85IOX0uwXbo3BE6xnhWOj+EqGFz15YYak8CMFhzRepOGRpmfMj7PP5c2vJJ83nTh4WQoeKTik8SINj9EzZjTZ78/Foyj5SQo/UnAwOwYPS0Ivu8mFl2JE+nwyePThwj4Sw4tdGvp9lsKPFBysIQYPS8L0RgJFEggMHDERPNEjePr06UNZKJDNYsyYMb6ZWxA00rt3b8r8AGp27Nihpk2bVlQT2Ip4q1y5snr++ecp+AITrEnw8qc//YnGgawnKGniF7mIoBFkSkAAAAIYpk+fTph43JgQ79Spk3rttdd8zwPcKHWD68WdaaVdu3bq/vvvJ0v461//SkESPD63HnW0UecYNxJkXUHwDLLBsByQQQUlfJYvX15wfuxlhBAEg0woPC6vHgFKwGMPgvnhhx/oOA6CQbkbr+Pzud7g+ZkCyHTiJ05/Vq9ePYWJDgRv2dupU6cUyllt374973pn8Hjfb5LkJ05e4MdRugsZvDZu3Fhwv4zrGzzeepYkP4YXPXnh5wop/EjBIY0XaXik6RnzY++jvvmV5POMffzO75VS8EjBwfwYPEW/e+lmN4YXPXmRZjdGz4ye+d23Wd9z7aPqGa6b9O8abvIxeIrsRjd+DC968sL+Qwo/UnBI40UqHsZl+mL/5nZfjls+gYEj9kk98zk7Cdx9990Kfwj0QLYNr4bgCmQaQWAC2jfffKOmTJmizp8/nznkmmuuoQmo119/XdWpU0fhCz0yZBQyWwcCQa699tpMeZbM4BwfkK0CZW0QYAGF/de//kWTdvbdUOoFOFC+BxkHatSooRDwgIljbjfccAMFdqCUzLFjxyhQI068wAJMKMn08ssvU4ALX9vZQ/4ouQNM2B8BFZs3b87sBkN66aWX1KxZs2jSG2+2IQjDjiezcx4+3HbbbZkgGOjId99953sVezYYDoJBmR1uQ4cOVVu3blXLli3jVQXtDZ7ibD268BOHP8NNDefp2LEjBYZAqeAjgBF+kJcRfIUAtXw2g6f0/UYHfuLghfUG5ciQKatKlSoK5bugU/b7arVq1QJ9JZ8ram/wlNYzlmWS/Bhe9OSFdUMKP1JwSONFGh5pesb8cB/lzS8dnmd4/M5eCh4pOJgfg8eqW63R9zTDC0tAT154dFLsRgoOabxIwxNFz1gGSX5v5jE4e4OnWCI68WN40ZMXHpUUfqTgkMaLVDyMy/SFl0Bg4EjckSpl6Xx4m37gwIEUaDB+/Hgq/eHEj4CJXr16USAI6EcGi6lTp5Im4C1pBFegx7mqV69OX+jPnj2rDh8+rDZt2qQ++eSTvL+Zjx8RMG4EcvTo0YMmeceOHau+/fbbEm9wI9oQWUTuvPNOGhMCLN5991319ddfq1atWqkrr7wygwelKZCJBAEhwHLo0CEKuECQDbKMILCkefPmFHixZs0ammhG0MjEiRNpnVOOUZY5iwvwISMKAiXczoPsKA888ADVOQOm9957T3355ZeUqQMTj8BVv359dfvttxOOyy+/nCbBd+3aRVziGLfzslzj6BHYguwUuBaCYDAR73VeBMw4g2DAEe8PHjkIZtu2beqKK64gmSMQJt84+PwGT8kgJR34ycWfoVQVvkwhqxK4RYOOYjIf2XEQ6ASf0KVLF3XzzTfTdmRcymfmEYOn+H6jEz+58sJ+jHsu94RllBXDfRM9gi9RegzBiwsWLMj4Pz4urt7gKalnTrkmxY/hRU9eWD+k8CMFhzRepOGRpmfMD/oob7Lp9rwpEY/h5WftvtcYPSv6vcrYf36/Pxs9M3qGH2ry/TuN0bMiPbPLAb+TJvW92TkOLEd5DnCex+Bx59kpp2yWDS+l38Q3emb0jOeZvHpjN3rbjRdvZn1p3rK5X4SVX2DgCM3gmX+RJfDggw+qtm3bUhkUZKPgrA7IotG6dWsq18Bv2iPQYMaMGercuXN0DIIVEJyBYBIEJ5w8eZImuNCDYG4VK1akgAEElOS7YeIXQSAIIpg7d65CYAECMJDBAlkiME40vNX9zjvvUDAGliED4AUW/KEUCo7HJJ69tWzZkoJGgAkZVT744AOFSc1GjRpRYASCR1AiJq7MIyiD06FDB8IzefJkdeTIkcxwMAYEsGBMaAh0QfYUTOLjR1pkKylfvjxNQkIeVatWVYsXL6ZAGATDYNzOBlkhsAQBQ/jDMVyGx7lvNsuXXXYZlTKCk4AOoWSOW8OkvDMI5quvvqJJUwQmIUgJZWJQxgfBPPYgGHxBhAwK0QyeS8mmEaSkEz9R/RnsBIFN0C006Bb8IQLHnA02h4d7ZOsZNWoU+UPnPnEtGzxF9xvd+InKi5dewO9Br+AfnW39+vVq/vz5Je6pzn1yXTZ4ivTMS45J8WN40ZMX1hMp/EjBIY0XaXik6Rn4ifImm27PM6xnkvAYXhR9/9fxe43RMz2/b0rihX2aFD8gBYc0XqThiaJnLANnn9T3Zvs4DB67NEp+TpIfw0tJLuxLSfLC45DCjxQc0niRiodxmT45CQQGjtgDFDKRK9Z4EbbAyzx8XsbUjNleJB9Erg0ZMoQm5JEN4uDBgyQ3TNDbJ7GQVQNBEpiYZ/nhjQkEkaChdAiyXHz88ceZ7SzvwYMHUyp+TDLv3r1bXWqVTblgXYu30wmsf7zM5+flbLbjB7n+/ftTeRkch/ECo70dPXqUghc4Iwlv4+tVtcoGPPf88+p///d/KTDGvh1vgKNsDxRzx44dhBcBNWgITsGPNMePH1ejR4+mdbnqJ8YE+aFcDjIgfPHFFxSUgsCQq6++mn60xIUQGILyNP/9738z8sc+COLBeJBpBcsIamH5IjDk1ltvLREowkFCHNmFc2MSfcyYMfhYlNEDvfXH8sJ6NF7m8/Ny0VZF2Ro4CAbjsMsfQTAPPfRQqSAYBO5g3JAruMWb+PYgGIyNg2Dwhj5KjEB3MeEPnbUHqPB4vMaX7XZkn3DDg/NX8MHTyMLzmA+eI1ZQz2krqMcLT1zjZ7yS+Iniz6DzAwYMoKw8kAXseubMmRRc5ma/Fa2gupH/8z8ktjfffFPt3LmTPrM84+QnCp5yFp7+Pnic40OQ4P/Y8Oyy8ESx7zD4o+DRkZ9LrZJgWd83LV9OftXSFjf52t80IIWy/q1bty4TNBJGvn7n9zseX66i4uGxOs/vhWeBFQRz0Qos5f29js9luxR+otiL076d8vXkxcpqg2fAoONz2R4Fj3P8zusH4Qk6PpftUfA4x++8fhCeoOOjbDf2b32vMf6Z7jf59M9R7MVpH079DrKXoONz3c6ZLnEe3K+D5Kfz8xmeH260Mnc+bGXuZDwctOplHzo+n+H56sYbb1QPP/xwBgeytvnd3/B7Bn47QHZONP4ewL9xOPHjO+vIkSNpX3wPwO8AUZ+/cBLn+XmZLmD988LD23l/tg8vPOet32zcnj+98Hidn68XdbsXHh6/8/xeeJLmx9i/vt+fYY9S+PGyFy/787IXY//u37/Z33j5n6DtUvjJ9v7vpX8sr7Q9nwXxH4Qn6Phct0vhx8teosrHi5eg59cg/Q273QtP2OPZXhi/F56g7xd8PJ8v6vW98EQ9vxeefPOT7f0/CJ8XjqDvF8xH0PmDtkuxf5aHNH6C+DPb8/P8xfqEPjBwhJ2i6aNLAD8ad+vWjbJuOM+CIItFixaVmIB37oPjMfH41ltvZSZR7fs0tgIqkP0C+yFjBrJaIMjk/ffft+8W22coDQIluCQNnxgZT1B64tNPP6VJYV7v7Fu0aKF69uxJJVXwxRMN9bnxoyCCFpD5Apk4cP5mzZpR5hLOTNKoUSNSWp5Mdp47yjImdvE2OjKpuDVkVUF2FL8sJwi8QDAFyxzjHzRoEHGBLDPYhj/wjb5Jkyaqa9euFKyCTB4I+Mm1gRdcM2wQjP2ayAQBfAiCARcIJkEGFm748XLYsGGUJQWZIPADNX4IW758Of3xfnH2Bk+xTujETzb+DBz269eP9B26AVtC0EhQ5poXX3yRUkAjgw98Sj6bwaMnP9nwEkY/cL6nnnpK1a5dm3YvRKYR+7gMHrs0Sn9Oih/DS2ku7GuS4oXHIIUfKTik8SINjzQ9Az/4ToLvtfwjKHPm7NPwvCkJj+GlCamgjt9rjJ7p+X1TEi+k/NY/KX5ACg5pvEjDE1bPGLdfn/T3M4zN4PFmKEl+DC968sKjksKPFBzSeJGKh3GZPhkJ4HcWBJCgXWKV5LDm8Ysm8tHj7RH09kgTsxxdHgh6QJBHnTp1KHgAJVs2b95MMvaTa61atdQzzzyj/t//+39UvsGND5wbE7R4SMG5Zs+eTdkz/M7rdp5s9q9iZQ5B1gZk1kAmlQMHDigEjwSdF4EjyCTxxhtv0FiR2QNvYyOQYeLEiZnjEZwwcOBABfyvv/46nTub8QWNw7kdEaDXXXcdYQKO/fv3UzDLnj17Au3gT3/6E5Wp2bBhA40fY0fmFAS+IJjkk08+yeBCFpLf/OY3FDQydepUFeb8YXEjawgyiwQFwSBTihM/L0OPEOCCDDj264Jn4AFGNAT/oIwPSuNAj/n4OHuDp7S/0YWfMP7svvvuU7fffjvpC94Y5HJHdr1y6guCrkaMGEH6NGfOHNI3v/2dx0ddNnj05CcML0H6AX+M4D4E7KHRm8v8huwl8df89BuPwVNa3jrwY3jRkxf251L4kYJDGi/S8EjTM+bHr0/T86YfDn5+kIJHCg5pvEjDY/TsZ3rpKonvz8afXSjxe5lTHkn9ruEch9uysRu97Yb9tLPX4Xuzmz45xxl22eAp/XtvnPINy4NzP8OLnrwwT1L4kYJDGi9S8TAu02fn3wIDR2iWxfxLXAIIaNi4caPnOLC9h5X+Fg8ZHDRi3xnZIRC0glIkCIjg9KP2fQr5uWbNmhScgGui3hyCHZBZABkG7O2uu+5SnazsJsi2grd6dG1t27YluSJ4hhtugvbgEWRiueWWWzJBI3FlGuHr2Xukp3IGweANe3uWEfv+9s8cBPP555/bV5f6jDcA//CHP1DAEAJ+8tkMnmLp6shP8eiKP1177bWUCQlrEFg2YcIE30xEfCTbPJZRkgqlknRoBk8RC7ry46UjCKZERi4OGil0phGvcUVdb/BElVx+jzO85Fe+uZ5dCj9ScDCfBg9LQq8+bbyY57Mi/dHt+czwoicv7G2k8CMFhzRepOExemb8Get0kn3ans+CZGXwBEkome2Gl2TkHvaqUviRgoN5M3hYEqaXJoHAwBETiZNdJE4S8mrdunWpoBEeB+pwPvjgg5SBAoEMaMg2geCSTM1gK9iE90+iR0DLE088oT7++GPKcsERtlDO3//+9+qKK65QJWocJzxeHl+Y3h488vXXXytkW/npp59U3JlG4uQNwS0or3P99derlStXqjNnzpB+oFxNIyuzzfbt22kZJX4QxIAgpFdeeSUvGUfiwGXwFJ4f2MbTTz9N2YLOnz+vxowZQ1mWgvhEmSpkV8JDFzLxINgkjJ0FnTfX7QZP0X1QV368+DUR7Ho/v0jhRwoOtiODR0+7MbzoyYs0u0mbnpnnMz2fzwwvevLC/koKP1JwSONFGh6jZ8afQQdYr5Pq0/Z8FiQng0fP7zWGFz15YXuSwo8UHNJ4kYqHcZk+mn8LDByRFikjDY9fphFk9cCb1uhPnTqlkPHi9OnTqn379pTOEm/zHz9+PHGRQAmHDh1KJW/++c9/qiNHjtCYOnbsqDp37qy+//57NWrUqFDZChIH4zIA3BSfeuopCoDhoJEwmT9cTlWwVdCrXr16qU2bNqm3336brtuuXTvVrVs3WvfZZ58plPZBcMmOHTsoEKZgg4twIYNnagSpRT8EAVJ9+vShEyxcuFCtWbMm8GQITBo0aJCqW7cufTFHFptvvvkm8LhC7GDwKKUzP246YCK+3aSizzop/EjBwZph8LAk9OoNL3rx4RyNFH7SiMM8n+n5fGZ40ZMX9l1S+JGCQxov0vAYPTP+jHU6qT6Nz2d+sjJ4/KST3DbDS3KyD3NlKfxIwcGcGTwsCdNLlUBg4IiJyIkWkVMIufllGrn88svVk08+qZAVAhkiZs2aRYEXGBcyeCAbwIYNG9S///1vLd7ox3gHDhxI4925cycFkdSuXZuyc6CkC7Kj6BDpHYXXW2+9lcrTYPxo77//vvrkk0+0xsM1TydNmkTlbYAbzgKld5Cqk+Vw9OhRNX36dPXtt98aPAV8E0F3frp27aruuOMO0pNXX32VSmT52S90C0FuzZo1IxtBppulS5dm9Iz1Lane4NGbH6de5BLBjvvjVVddpSpVqkTlx1Da7ezZs4n6N4On+DlMJ34ML3rywv4gF3787ld8/kL1UnCwvAyeYrsxepb7G7z5eD5L8j4jBY8UHOy3DJ7g7wFJ2I3hJZiXJO8z+eAnCTxScOTTnyXBi1Q8jIv7XJ6bk/DLPG6v3uAp/h6gEz+GFz15YTvKhZ8k/TOPn3spOAyeYnvRSb+YF9PHy09g4AjN4pl/2knAL9MIgkWGDBlCmUa++OILCg65ePFiBkPFihXVCy+8oBCgMWXKlMz6pD9gXPhihtI1wIBgkWXLlmUykCQ9vijXxxsKjzzyCAXAzJw5U912220UyLN27doopyvYMffcc4/q0KGD+r//+z8KCuELY0IVujV16lQqeYSAEThl3ZvBU1iGoPMtW7ZUBw8eVGPHjvW9OB4ee/furZo3b077IYMNgsXseoWsSVdeeaX66quvfM+Vr40Gj9782HmPGvEN34b7T8OGDe2no2xdKO0GvUyiGTxFUteNH8OLnrywjUblB8cnfb9hDOil4GBMBg9LwuhZsSRy+xTn85kO9xkpeKTgYO00eLy/ByRpN4YXb16gu0k/z8TJT5J4pODIhz9LkhepeBgX+qjPzUn6Zfv4nZ8NniKJ6MaP4UVPXth+ovKD45N+DmAM6KXgYEwGD0tCLz0rHpX5FJcEAgNH7JN3mUgi6+qYKuZlHgwvI6+C2Z4/+TgzjfznP/8hClj+KDGCfXgCloNGeDsyAdx7771UugZlJLjxdl35q1WrFpWx2GhNHqdFv3AzwaT4Rx99pPbs2VMkaivQgsePSXOUr0HTSf51rUl6lNf5/PPPKfCIx4eHXASOrF69Wi1evLjE5L5O42f5sv9C0AHjmWNl2eHtbnh01X+7fHXn5+5OndTdd9+tDhw4kAkcsY+f5e982EJpmilvvqnOnT+fsYdy5cqpZ599VlWpUoX0bsXy5SW2V6tWTX333XeZ/fPBXycbnnFWIAyPn/WL7cMNz5sWnvM2POUtPMNteJZbeOzbq1t4Ttjw4NxobvJzXh/LYfBL44fl4xbBPn/+/JKZa6yd7fxBruAXpdHwGQ1yhF9GeR5eBk/4CyNf+/npBNa/KPy54Vlg4bkInn/JcOQ8//9nrQe/YfE4j88nPjc8aeTnUuu+3rdvX9WkSRMS37p169SCBQsUnrW85IeHbfjEQF6sYNnlK1Z48ut1ftaHKNvht7zweOlHWDwI/l1hwxNlfBhDNvii8MPn18E/s/9ws5cg+2f5li9fXg0fPrz4vmlxcO7cuYx+Vq9eXZ04cYLpzUq+PL5s7z9ueILsnwfohYe3Mx7Gz3zydl6Oc7sbnjTyE8X+WZ5Sns/C+rPlBfDPcTyfhcWTT/+M+x1/Dxg3blzRc5hlkE7/gecsZBzk+yl9D7BeaiF/pdHzmRseN//yKx88Oj2fueFx84+68xPH97Ow9pIW+5fmnxlPks9nbvaSrf2zfXk9z/B2fp5xO7/Tf2Iflg8fz8t+x7vhcTvez/75/F54eDvjcTs/9uHx5rLdDQ9f335+P//M1/fCw9sZj9v54+KHn6+jPJ+F9Wf5vP/z+J38Rvl+FhZPIfyzk18p/Lh9nwn6fga7Mr+fxeO/YC/sX9hf2f1LFH74eC9/xtvZn/ldP2h8YbdHsX+WR5L3fy98Ueyf8ejw/TkO/8x4dOTHic/pv832LPyX5e9xLwbfJDfrDXHLLiDSookTDjigFeafNhK45ZZb1P33308T+sgoYm+XXXaZGjFiBE1E/u1vf1Nnzpyxb1Z16tRRgwYNUnBW//jHP1KTzQPjdf4IjslWbjx5zMtp6BH9OWDAAPXxxx9TEI9uY+a0nCh1tGTJEppIffjhh1W9evUUsqds3rw5M2Tse+zYMfXZZ59l1un2weApHD8NGjSg0lO4h0yYMEHt27evlDqgPFXPnj1Jn7Bx7969lMkGZUGc7eabb1bdu3enm9WPP/6oNm3apE6fPk1ZSJo2baqQwcceBOc8Ptdlg0dvfsAvHt7tkw/r169X/KXXi3/8IIbAvmuuuYZ2QbAIguJQxu3kyZMKWbC6dOmioH9oyIRTqMwjBk9RPW3d+DG86MkLGaj1Lwo/fCz3Sd9vMA4pOFimBg9Lorg3elYsi1w+5fp8pttzgBQ8UnCwbho8Jb8H6GI3hpeSvLC+cp/0fSZXfhgH90nhkYKD5Wjw6G034CnKc7Mufpn1zN4bPHp+fza86MkL204UfvhY7pO6b/L10UvBwZgMHpZEca+DnhWPxnyKWwKBgSMUYWVFlpi++A1fjqxKssekKybqnbygPATSKSLDxaJFi0psR0QhgkaqVq2qVq5cqZYuXVpie5J4nDjcltu0aVNq8hiTyAhk4MljvPWrOw4e34033qh++9vf0ngRnAFOkKEBRoltCAICR7x/oXtEt2LStH379iX8zu7du9XEiRMz46pfvz7pFcaHCVe8lc8ZIIABb5EhwKnQ43dez+ApLD8I9Gjbti1NwCPQaP/+/eRvMBmPjEjIeoQvuGgoSzVjxgyFoBEnb7xst/8SCmktlHjjP0/3K4NHX37cIvEzGSA89AHl0JBlAT+eoR06dIgC4o4fP17qvgjuoX+nTp1So0aN8tVT1tdceoPnZypXpxs/hhc9eeHnxSj8eNlpkvcbKTik8SINjyQ9i/p8putzgBQ8UnDwfcLgKfoeoJvdGF70/X6G+2ZUftjunH1Sz2dScLA8DR597SbK85lufpn1DL3Bo+f3Z8OLnryY75ve865J3f9z9Wf245lf9AaPiTOw64OXnuiwPjBwhGZUzL/USIAn8ZEaHCnouNWtW5cmyVDuYcuWLTRBBiVNW7NHsjnHHuYNc+cxSS/74cHY3n77bcqukOQ4Uc7lhhtuoIk8TP4ju4MzG5Edx4ULFyiTDTLCIKgHD6UcqJQkDr62wVMYfhCJO3jwYCovBX05ePAgBYXAF+Hmx23NmjXqgw8+KKVTvN3e2/WM1xfK7g2eiyxyzz4pfuCfkA0JLYw+IGCpf//+CvdLNGQRmTVrFqVGpxWOf/gxZuTIkbR2ipVCfefOnY494l00ePTkx/CiJy9sfdnyw8d59WnxZ17j5/VJ4eDrS+FFGh5JvER5PtP5OUAKHik42PYNnosU8K/b87PhRd/vZ7CdKPywzXn1STzXSMHBMjV49LWbbJ/PdH6egb4ZPHp+fza86MkL++hs+eHjvPok7psYixQcLFeDhyXh3ielZ+6jMWvjkkBg4EhaImDMOIsi8zAh++STTypkF3nvvffUt99+S84aWSNQ7gUlHmbPnp2pHZxGublF5hUi40C+Ir3c8MDAMQE6b948svU08OSFA1lhJk2aRMEkacDBPBs8uUeAImgIZbWQecTZjh49qj788EO1devWUhkevPQE5xs6dKiqXbs2na7Qdm/weEeAw26S5Ad1VpHhKCjTCMbYr18/yoQEJdq2bRsFUiLgzUvvsP6ll16ie+jixYvVqlWrPDPjsP/ItTd4mpCN68aP4UVPXtjewvLD+/v1afBnfuNnf5YkDh6fFF6k4ZHEC/Q87PNmGp4DpOCRgsPuT42e6fccUJb1jO9Lfj3kY74/m9812I9xn43d+OmX/XxGz3LXM8gz7PMZODS/a5jfacLYJ9upvTd6pt/zTBR+wvCf5HNAWD3THQePz+Dxvs8lqWfMj+m9+bH7l2zkFBg4Qr/cm3+pkkCtWrUou0iNGjUy48Yb/yghgswPaW+IkH/qqacyk8dh3jDXHfOtt96qfvOb32SGmVZMzghDBI1MnjxZHT58OIMtTR8MnnjYatiwoWrcuLGqU6eOQimQb775hjIfOTPX+F0Ndv/YY49lJvyTtBGDpzRTOvFTenTFa7p166batWtHK1Aiadq0aQpBI34NmbpGjBhBwSJz5syhklx++xdym8GjlI78GF705CWsbabFnwXhkYKDcRo8LAm9ep14CfN8lib/LAWPFBxseQaPns/PZZEX1kmvPm3+2QsHr9cBTxg94/F69Trg4LEZPCyJ4l4nfopHVfpTmp5nSo++9BqDR8/vz4YXPXkpbUHua9Liz9xHX7xWCg5GZPCwJEyvuwQCA0eiRqSY4/zfzM63fOCEMOmNzCMnT55UX375pcIkvv26V1xxhULZjkqVKimUIMHf2bNn8/4mdTaRTfbx4jhEsPXt2zczeVzojAPO8cSxjPSCzgnxNGUasfN5zTXXqD59+hBPac00YvDEH6GYq51Is3uDJ5n7I/wT7h9oKJ00fvx49dNPP5W4L9rtn/W2Y8eOqnPnznTc6NGj1aFDh7S4Txo8RXqkGz+GFz15YXsO6qX4Zyk4mC+DJ5n7Jsvfq08bL8Y/6+mfDS968sJ2L4UfKTiYl6A+bf65rOAxvOj5PMP6lxZ+pPkzg0fP5wDDi568sL8K6tPiz8oKDsYphRepeBiX6Yv8X2DgCM2UmH+iJIBgka5duypEl9vbqVOnqIzNjh077Ku1+SwtIg+ClYTp2muvVY888kgmaCTNmUbAjcEDKSTfJNkIpGnwJKNTeNgZNmyYQkau8+fPq7FjxyqUSwpqyNz19NNPE2979uxREydODDqkINsNniIx68aP4UVPXsIapRT/LAUH82bwsCT06tPGi/HPRfpj7pv5tSOjZ0bP8qth4c6eNv8chEoKHik4mC+DhyVR2N7cZ4rkrdvzDGuBFH6k4JDGC+MJ6qX4Zyk4mC+DhyVh+rRIAPcCvGCLdknLli2tgJqfaQE9ygjwMu1g7Yhl7I69+M1cOsC2bLbrKR+Qfffddyu8ncukg0+8cY2sF2hYXr5smVq+YoVW/MK5emUaoYFb/1gf06J/nplGLA7SZl8IsvDKNJJGfq7xwZMW/WJ7gPzTyo9rJO78+eoi7kO/3I/SpF9ueOZbeOi+msL7qxueBZryYz3fkI+CvixYsECtXbs28PkG953BgwerunXr0r4TJkxQe60ySzr4Zy88fv7JDQ/KRqGxPfkdn8/nPy88NDiP8bnhSZqfli1aqD6PPkrDhp6tWbOGPvvJF88CpfRs795A/QQf3PzOj32ibm9h4XnUhmethSdI/3/lgycp/QqD382fpdE/X+rzvKyz/L38i7Tnfyn8uNmLrvd/tn/jn63gZQ39s5T7P/tXLzy8nfXRfv92e54xz2dFEmJ5+cnP6/5hf/6RZv92/XHiT6N/9uPXDY95PovXPvzk79QvLKOZ57Po36+K2It+vLTvZ9L8sxR+vJ5n/PTX7Xkm6d9n2L954eHt/Lxhx+eGR5fnMx6v2/il+Gcp93/+/cwNj+7fn930CzYC/XPDk8bnM+aHn28Yn9fzT5nabvEcGDgCgZiWfglgYqJ3794KqcbQECyyevVqtWHDBiplU7FiRdWlSxcqb4Pt06ZNU7pkHsFNz1nKhZ0RxprGJgmTycyhtwamlZ9cbKR27dqZMlwHDhxQ+/btU+fOnUuUKIMnUfGre++9V7Vv354m41999VV1+vRp3wHhIRgT5s2aNaP9Vq1apZYsWVLimCT1zODRk5988FJC6Qq8IA2Pl/ik+GcpOJinXPDwOXTqpeBJK458+DPzHJC7hRlegp9ncpdy9DPkg5/oo4l+pBQcQRLIxT8n6c+8cEnBkwsOL9kkud7gSVL6yvyuYX6nKcjvm9Lum9LweHmhXPyzTs8BUnAwT7ng4XPo1EvDo5NsdRtLYOAIRdjwm9Cmp0mnTGRfSuRRoUIFytbRoEED0r9Dhw6pmTNnquPHj5fC0717d9WmTRuFsjWjRo1SZ8+eLX5TNQG8bhFseIsX2XCCeLjiiisyk8f79+9X+EsaD+zJM9OIxU4Ye4PRhsEfJJ84tiMQySvTSBznDyOPOK9j8BRn8ohTrtnyGNXu69WrR1+kvcpwbd++PdBv5AO3wVOsVygVgwweGzduDOXv4uIDfgpvGRw8eFCNGTPGVw/AFwItmzdvTvdMBFEimJL9rg56ZvDoyU+cvMBvJmUvbHfS8DAuey/FP0vBwc8LUfHoYDd2/ZKGJ828xOnPzHNA8XOdm76z3ofpDS/ezzM6+LM4+UkSjxQcfvYW1T/r4M/ccEnBExVHkvbixgf7c4On+P6X1Pe0OP2ZDvZv8Hg/ByTJT5y86ODPpOFx89NR/XOSeiYZh7lvFt8vmeek7pt8fdP/Ukkmi/n9wMAR3SJdzHiykwCCFPr376/q16+fmQCbNWuW59v3CDIZOXIk7TtlyhS1c+fO7C4Y495RI9iuuuoq1bVrV+U1eZxkJpWomMqXL69uv/121bRpU5p0RWAPsigsX75cffvttzFKPfypomayqFmzJk3ggidMxALHV199RZlvwl89/j2j4ol/JPGcMa14otgIbv6hynBZ9rLCKsNVyGbwFEu7XLlyavjw4apKlSqU8QpcnD9/PrNDtWrV1HfffZdZjvMD9AN/yEAzbtw4z1PjnolMI02aNKF9kIoS90KMUyc9M3j05CcOXlg5k7QXHoM0PIyLeyn+WQqOXHjhY3WwGx4L91H44WN1wpN2HHH4M/McwJoZX294Kf08w9LVwf7j4EcHPFJwsCydfRT/rJM/k4onCi8sCx3sn8fCvcHDklAqSX7i8Gc62b/BU/o5QAd+4uCFLSZJe+ExSMPDuLiP4p910DMeP/dScOSCh4/VwW54LNxH4YeP1REPj8303hIIDBzhCCnTl46UgpPVWS6INuzXr19mAmzbtm2UaeTChQu+437ppZfoQXjx4sUK6fmTwBklUhLKjIeBjh070pih9uAHZXkwGcjLCLbAX6FxRc00ggwwCIRBOSFnA7YPPvhArV27tqB4omTmqFSpkrrnnnuoHBK4srczZ86oefPmUQBJoXnB9aLg4XEi5d3Ro0ep7JMu/iCteKLYPR4+vMpwIQgBeucsw1WozCMGT+n7JvwZMlvBfn788Ue1adMm6hHljsA4+DJklWL7iqtHIOHAgQMpWG38+PGUgcpprzVq1FC9evVSGAva3r171dSpUxX8k256ZvDoyU+uvDj1PSl74XFIw8O40Evxz1JwsD+OgsfOK86TtN3YxyMFjwQcufoz8xwwTeXj+dnwUvJ5xu4/dPBnufKjCx4pOJzyxHIU/6ybP7PjkoInCg67HHSwf/t4DB7zu4ZdH/i5PY4+V/+smz+TgidXHE59Sfr7mTQ8dvlG8c+62Q3wSMHBfjEKHjuv5jmg9H3XKR+znP+4hMDAEfvkrvmcLgl069ZNtWvXjga9a9cuSrWPoBG/hjfBR4wYQZN2c+bMoclwv/3zsS1KBBuCMrwmj0+ePElBF87J40JmHomCCcc88MAD6qabbsqIGdk5tmzZQvwgowSydqBhohUTroVoUTJZYCL2kUceUcgowA1YMCGLVFW8fsmSJRSsxPsUoo+Ch8d19dVXq0GDBhEfGzZsoIAk6Bta5cqVVePGjdWXX37JuxekTyueKDaCTDx9+/ZV9jJcyKh07NixUrJ2luE6d+5cqX3iXGHweEvz5ptvzgSPOPdav369mj9/PgX9Obfluvzggw+qtm3bUnYj6Al8EBoybbVu3ZrKHHGQIe6ZM2bMoOxcuuqZwaMnP1F58dLvpOyFxyMND3BJ8c9ScLCuRcHDxzr7pO0G45GCRwoOcBLVn5nngOIytvl4fja8FD3PQEfdWtL+LCo/bliwLik8UnDY5RrFP+vqz4BLCp4oOOy82j8nZS/2MRg8dmmU/JwUP1H9ma72b/CY3zXy+TsgW21UPePjnX1S9m8fRxT/rKMfkIKDuYmCh4919mnVMycOXtYBD4/F9MESCAwc4Ugp06cr0gkZBzChinbw4EGFN6yRnSKIR2Tr6Ny5Mx03evRodejQodjf/PaLCIsSkYdJP+fk8cyZM9Xx48dL4XVOHp89ezbv+KJkGsExwNSoUSPiAsEIc+fOVcgaw/KD8d51112UZQXcovzC4cOHM9t5vzj7KJksWrRoQW/x48aJtnXrVrVw4UJ14sQJ4gdYgaNDhw6UDWDChAn0pn+c4/bS+yh4nOOyR04jMOvIkSNU1gLBMtDnlStXKgTEOI/Lx3Ka8dx4443q4YcfJh1Zt24dBUOhlJGXnBAh7SzDBbtHSRE3vpGxh8twvfnmmwqBAW77eV0v2/VlHU+QvOx2Q6Rb/8LwHnRev+2wxyFDhlC5L+gW7o3Yv27dutTzONasWUOZnGDPOuuZwaMnP1F48dNb+Kkk7IX9ozQ8wCXFP0vBwfqfLR4+zqtP0m6i6JkXDl6fFB5JvETxZ+Y5IP/Pz4aXC57fd5K2f1w/Cj88bq8+CX8mBQc/n0W5z+jszyThkXTfjMKLl93z+iTsPxe74XF79UngieLPdLZ/g8f8roGXYf1+//Wyv2zWR9GzoPMnYf+5+DNd/UC2901dcbC+ZIuHj/Pq06ZnXjh4fdJ4eBymD85YEhg4whMppk+PBEDqsGHDKJMDJlHHjh1LpTSCECBN/9NPP02R/nv27FETJ04MOiT27TfccENm8jhMxCmCDpyTx3iT3OttKASZ8OTxlClT1M6dO2PHYD8huLCXCwqDiW+AyGaB9s0339Cb7yjr4GxwcoMHD6bMIxs3blTvvPOOc5fYlqNksrjuuutUjx49FOSAh0AEjGBy2K099thjCtdA8AuClvLdouDxGpNXxCQ4mzx5MmHyOjau9RLwoNQUMrUEZZxw2pW9DJefPF988cVMGa7Vq1f77RrLtrKKJ4zwEEj21FNPqdq1a9PuYXxjmPMG7YPrIhsXMo84G0pOLVq0iLI6YVsa9MzgcS/H5+TWvlwIP5ANL/axeX1Oyl54PNLwAJcU/ywFB+taWDy8v1+ftN1gbFLwSMEBTrLxZ+Y5ABJTSrf7pjReiqTs/z9pf5aN3fgjKdqaFB4pOOwyDuuf02A3wCUFT1gcdi69PidlL/bxGDx2aZT8nBQ/2fizNNi/wWN+1yhpWflZykbPwowgKfu3jy2sf9bdD0jBwdyExcP7+/Vp0jM/HLxNBzw8FtP7SwB+A3PPaJe0bNnSClz7mRbQY6KXl2kHa0csY3fsxZE5dIBt2WxPVj4trQwPfR59lGhB1CbenkZjvtz4QQAGAhDw5jU45qwPSfDfqVMnddlll2UyDtDgXcaPaFF7UEZm8hiZVWz7O49/6aWXMpPHq1at4s2+8sFOfvLz2w48uGFgYnTevHmWcvzsOT5gQiAM6u+hMSZkFPG6PrLE3HPPPZTB4/XXX8+bfSIrCLLRIBhi0qRJlFnDTz8sf0Llg+BkMH5kg6CMKRYuN34aW2VdnnjiCcL95z//mcrY+J2f5UEHWP942U2/sY9zuxeesMfz+fj6zohJlhMCYdyu7zyel6Ne3wsPjy/b83vhOWLhceMv2/Pz/lHGd5818X/77bfTofYyXNAXL/lVrVq1VBmufOpXNvgQyOCFx0s+Xni88PN4dNh+qRW8gYxKTZo0IXglMo1YawqhX40aNaJSUldccQVlp9q7d6/avHmz+hnPPdYYIK/77ruvNC8B95eqKPf23HN0PMq9ffbZZ4Qx3/JnPHUsPMesbFuMB89xaF544Jt5u5v9oHzdczY8Gyw8LJ982g/jSTs/uJdDz+vUqUMltPZagaCbrXJzkF1Y+eFLlZe9EHm/8OvGX9zbG1l4GtvwILAV5fMYTxj/ooP9sz2GkY8U/+zmz3S1/2z4AYe8v13/8Dxdym6sMmgX8Zxg+few9ud1/kIf74aHg2wZjx1/oceH64W5vk72H8Y/p8n+w/jnNDw/S7n/8/2F8fg9nwXZj072z3j8ns+C/I8bngUF9s9h7D/I/+v0fBbG/6bB/vl+xvbDy2740uSf3cYfpF+83c1e0nj/5++PbngKbf9h9Ivlnwb/LOX+z/Yexj+nyf6l8CPl/s/2z3ikPJ+x/TA+Xrbff9x+D7ig6e+bbuPn58s02b9d/jz+sPcXvv/o9P3ZT7/C4kvb8zM/v4TFly2/Wp//kkvohVrYI+FyCxxhpTB9eiRw7733qvbt29OPoq+++qo6ffq07+Dx8P6oFWjSrFkz2g/BFCitYW94K/yqq65SlSpVUgcOHFD79u3zzOphPy6fn3GzaNeuHV3CPnnsd01Mgo0YMSIzqbdhwwa/3WPbhhIi27dvL/FDtdvJe/furVq1akWbsP+MGTMUyiX4tTvvvFN16dKFgjNefvnlwGv4nSto2x133EE4OBjCa/8GDRqoAQMGUGpbTExMnz49MLvLlVdeSRkIcE5ku0HWm3y3sHjCjCPOjB9hrue2jzQ8bhghZ2SnQUOpEQS5IbNSUENgDYK40JDRJkiHaccC/JOGJ0hkeEgEfxw0UqhMI0Hjcm6XxovBU8Swrn7AqX+8nBZ74fEG9WnDI8VupOAI0i/enjY943F79VLwpA2HNLuRgkcKDi97d65Pm904x+9cloInbTik2Y00PE474eW06RmP26s3eLwkk9/10uzF4CnSF/O7Rn7tJujsafNnUuxGCo4g/eLtadMzHrdXLw2PF05J60NlHMlEelkRJhTxZHqt5dCnTx+FbA+YVB0zZkzmzTo3HhE0gmCF5s2bk17v2LFDTZs2LVNbrl69egqBKIj6tbdTp06p2bNnUxCB23nzrScIxMCbhGjAOX78eAqcCLousnMgawYaJo8PHTrkK5+g88W5HZP+kDUagiZQSgcT4kHyHTJkiEJZGwTz/POf/wzcP+h8uW5HcM7QoUOp5AjedkemEbyRHHReZFxAMBAaAp6QsSNO+QZdP5ft0EfYHeyJM40cOXIkNeN3yllXPOAI5bRq1apFtgH/duzYsUA5V69eXT3zzDOZMlwINsmFb6e8oi5LwxMkB9c3jApQyzRoXM7t0ngxeIoyDOjqB5z6x8tpsRceb1CfNjxS7EYKjiD94u1p0zMet1cvBU/acEizGyl4pODwsnfn+rTZjXP8zmUpeNKGQ5rdSMPjtBNeTpue8bi9eoOnOOMddNhLTnGvl2YvBo/5XaOQ9uNlj2nzZ1LsRgoOL71yrk+bnjnH71yWhseJT+pyYOAIzeKaf6mSAMqi4A+ZQcaNG+c5dpSnQaYRfvMbKcftwQo4BwIt4JzRYATIHoHjeHn58uVqxYoVtFyof1DaYcOGZSaPx44dq44ePRp4+Ro1atCkMyLcEJiBrBa6NJSdePbZZ2li+/vvv6eglqBMMRh706ZN1eOPP04wwMOyZcvoc5L/kE0AUaBoc+fOzZRq8BsTbiDgtGbNmhQIMGrUKL/dtdoGrI888kgmaGTy5MnaZLOIIiid8bRAGS4rQAdt4cKFmTJcfjjhrwYNGpQpwwW7h6/ToUnD4yfTNEUWS+PF4FH03KKrH3CzmzTZi9v4nevSiEeK3UjB4dQpt+U06pkbDl4nBU8acUizGyl4pOBgG/fr02g3ZQFPGnmRZjfS8LjZTRr1zA0HrzN4WBKF76XZi8FjftcovBWVvGIa/ZkUu5GCo6RGuS+lUc/ckRStlYbHD6u0bYGBI1IjZiTjQnaQgQMHUtYQZOLYv39/qYhmBFH06tVLIaMI2t69e9XUqVPVmTNnVLly5SgLCbIOoCFYZPXq1QplXb777jsqV4PSKDfffDNtR4YSlFUpVOQnsqnw5PEC6231tWvXlsLn5BdOavDgwZnJY2QcAGbnfkktP/TQQyRPXB/BOzt37gyUJ8oGIbNHtWrViLe//e1vVJaoUDy4Xad+/fo0SQ/FQJYRlKhx288pZ2SBQZASGjLZfP7556GOc56n0Mu6ZuaIKgfd8XTt2lUhMw/wcVYaP/3CDQ6BTFyGa+XKlWrp0qUl7B41urkMF3wl/s6ePVsQ/csHHj95RNWLXI/LJbI4CX7ywUsSOJg3gyfYD+hkN2mzF9Yzrz4XPEnyIsVu8oEjSV7yoWdJ+mfpeIz9F79hnKSeSfEDUnB42T2vz8VuktQzHr+zzwWPTvebtOLIh90kqWf5wCNFz5LkxWn3vJxWu+HxO/u04cmHvSSpZ/nAk6T9S8MTp70kqWdOHLycNvvncedDz5LgJx84krR/5sfZ56JnSfDiHL9zORc8OvLjxCd9OTBwhGZyzb/USeDBBx9Ubdu2VSdPnlSzZs2iMiYAUaFCBdW6dWsqicKZQ3bt2qVmzJihzp07p8qXL08lYBo0aECYUcoFx6MchLN1795dtWnTRqFsDTJE4PhCNJRzad++fWbyOCgzB5wUMqvw5PGqVavUkiVLCjHUUNeAI3zhhReIm61bt1KwRdCBwNS/f/9MCaGwmT2Czpvr9h49eqjrr7+ego3++te/UtmWoHMiEAjlkiAHlB1Clhw4Xt2bzpk5osguDXiQ2YXLcCHTkF+DjbiV4WLdQrAIHjy9ynChbFe+W5x4MFZk7LnyyivVV199le+hhz5/1MjiJPmJk5ckcTBJBk/pcnzsByAjnewmjfbCeubWR8WjAy9S7CZOHDrwEqee6eCfJeMx9l/0XUYHPZPiB6TgcLN7XhfVbnTQM8Zg76PiwTkkPJ/pgCNOu9FBz+LEowM/GAO3qPaiAy+Mwd5HxYNz6GT/jCmNeOK0Fx30LE48OuiZNDxsK+ij2osOembHwZ+j4sHxSfuzOPUsSX7ixKEDL6xb9j6qniXJi338zs9R8eA8SduNE0tZXQ4MHJEeOSMVHyZNhwwZQhk2Ll68SBPymJivW7cuTdCzwq9Zs0Z98MEH6sKFC5RpBMEIyBqBhonTmTNnqvPnz5d4Q58jvipWrKhGjhxJ+7755psKASiFkCeyjfDk8ZgxY3wzA3hNHkMmjIPlgqCFQozffl1cD84QZWrQ3n//ffXpp5/6jgOYIANM9KN98cUX6t133y2BB/uA00LjGT58OOHZvXu3mjRpUuD1GzVqpPr160cPlch2g2CAEydOZI67/PLLVatWrSgzDrDs27ePJsWR+cYpx0IuR83MIQ1PofXLWYbL6/rOhxMuw4XgNtz0wpbhQimufOpVHHh4fAj6g/1VqVKFMkShdBXw8vbq1auTbfFyIXr4ob59+2bKoa1bt04hS5TT/9p51IGfOHjRAQfL1eBpQvdKux9g/dfJbtJqL6xnzj4KHp14kWI3ceDQiZc49Ewn/ywVj7F/87zJfiPO3viz4gw2LFdp/oxxpf35TCcccdiNTnoWBx6d+OHngCj3TZ14YRzcR8GjIy9pxxOHveikZ3Hg0UnPpOHJxV500jPGwX3a/VkceqYDP3Hg0Mn+Wb+4j6JnOvDC43f2UfDozI8TX1lZho6BF7RLrMl4C3fR2zHoMbljWnolgMnTbt26UeYRJ4qjR4+qRYsWUTkRbIMiYAK/SZOiCZZt27ZR0AiCD/zaiy++SAEnixcvpslKv33j2ua8WXidFxlVkGmEMfGkEQJh7A0ZTG6//XYqYYGSPGwD9n3y+RkTvc899xxdAsEWCLrwauAUUZZcRgj7osQQyglxQyDKgAED1Mcff0xBKLy+EP3zzz+vKleurDBpvWzZMt9LghfwA56gZ8h6g5JHaAhKQjmkm266iXTTfiIEmMybN09t3LjRvrpgn6Nk5pCGh4WNjB3IRvTZZ5/xqrz2yIQ08JcyXCg3hUAiZ0NwTrMRl6QAAEAASURBVM+ePUuV4UL5GegaspCw/djLcCE7E/NkL8OVz8wjueJxYse4kQkKN/Uff/xRbdq0icpXIQtJ06ZNqazXwoULnYflZdkZvLN+/Xo1f/58X/+qCz+58qILDibW4Ckuxwc/4Gw62E2a7cUpTyxHweM8T9K8SLGbXHHoxot9PFH0TDf/LBFPFF7scsBnY/9OieS2LMUPSMHhxmYUu5Hmz5xySdoPYDxReNENR652o5ue5YpHN36i6pluvNjlKsFupODJ1V5007Nc8dh5xeek7zPS8ECmUexfNz0DDm5R8PCx3Kddz3ThR6K9sI5E0TNdeGEM9j4KHvvx+Jy03TjHU1aXAwNHMInOET+mvySTCSFNckFWh8aNG6s6derQJO/evXvV5s2bCQvjuO+++yh4AoaAzCHTpk0LzFhRtWpVNWLECNKPOXPmqA0bNhREPigtwZPH48ePV/v37y913Ro1aqhevXqVmjxG0IFTj1Fuhydbjxw5QsEWyHqBAIhbb72VJqfxlrzzuDiXETiCABK/jCOY1EamEfCJhknzyZMnl8oIc+ONN6rf/va3NF4EYqxcuVIdOHCAAjCw7bLLLqMgmTjHz3r02GOPUSYUBBKgfA6vd/bNmzcnfnAzwbZ33nmHJrnxGSm2EBxTrVo1wslYwV2tWrUy61FuCNjygcM5Xl6OkmlEGh6WNzITDRo0iOQP20d2Ds4EAx1DYBCy4fD+cfWwVS7DhYxIbP+wD68yXJgsRpkuZL/AwyYaynDh+OPHj5fSU2cZLhwf1/id54mKx3keXrb7MwJq+xcm4wefJ5c+SmSxbvxE5UU3HMyjwTND+dlxknYjwV5Yz9BHwWM/nu+36JPkBdeXYjdRcejKS1Q909U/S8Jj7N88b+KxM5/Pm8afFf0eJc2f6Xi/Mf7M+LN8+zNJ939+fpdkN1H50c2fRb1v6nqfiYpHN154PJLwRLF/XfVMiv3nqme68SPJXnK5b+rGC+uZNLux42K+ylofGDhim3MyH4VKABkUMOGPhnIteJPfmZXDDfpdd92lOnXqRJtGjx6tDh8+7LZbXtY9+OCDmcnjWbNmZTIPwHl6TR6jbINXs0eyOffBm/soifP99987N8W23L59e4XMJ5jYGjduHAX42E+OYJGHH35YIVgHbc+ePeqtt96i/e378Wc/PNjn7bffpkAN3j+u/vrrr1c9evSgDCjAgUAce4PDgc7ceeedNBGPrEYos8PZQ+wBJThu69atFEyDyX00BJog4wyOx7FeWSdo55j/Rck0Ig2PU6R2PUPWGPAN31GvXj2aPERgz9KlS52H5bQMHRg8eHDoMlzQE0TiOstwwW94+QT4ES7DNWXKFLVz586cxux3cBQ8fufDNjsvvG+YjB+8b679DTfcQP4K5wlzXR35icKLjjiYS4MnOINeUnYjwV5Yz9Bni8d+rNvnpHjBWKTYTRQcblzY1yXJC8aRrZ7p7J8l4cmWF7tOuX1OUs+i2I3OehYFjxsn9nVJ8CMFh12O2dqNznoGXNniscvC7XMSeiYJB7BEsRud9SwKHjfdsq9Li57pzAvkKcX+WTck4IliLzrrWRQ8zKdXn5T9YzyS8GRrLzrrGbjJFg+O8Wtp0zMd+ZFkL6wr2eqZjrwwFvTZ4rEf6/Y5SbtxG09ZWxcYOFLWImnKGl5ETz399NOUyQETvgiQQOmJIDlUr15dPfPMM/SQgyAGTOAXMhILkaxDhgwJPXmMSe2g8bm92YqgEWT1QHaCoONz2Q7H8/jjj1M5CWTWQPYGZAlBlhhkbkDQAs6P9vXXX1OwBcps+PHkhgfHYyIXpV7Q/I6PigfZQlq1aqV++OEHhUw0yHrCGShuu+02hbIZaNA3BLCgLBLGgYAfBJ3AKWGyHyU11q5d6yp3zmyCYCUELeUDhx1/lEwj0vDY5WGXt5eewXZQegnBJPb9vc6TzXrY//333+9ZhuvDDz+koCNcF/t6leHyG9dLL72UKcO1atUqVz30Oz5feMKcF5iHDh2qateuTbaWzzc/vcaDADHYPbI1wZ699tOZn7KsZzrzwnaXDT9e+mdfj/MlZTcS7IV5QR8Wj13+9uPt65PkBePIRs+wr673m2xw2OWvKy/Z6JnOvNjlG9ZudMcTFkca9Cwbu9Gdl2z9mc78ZMOLzjgk2j/LW4ofkIIjW/s3/iy/mZPYTrgPq2dp4CWb5zPG79cDc1Lfz3hcYfnh/f36pPDguub3M+8M8knxws8B2fDjp1/28yVlN2HtBZh1/d7McpTmz7LRM535yQaH7vbC4zN2o69/tvsD5qss9YGBIzTrZP6JlUCLFi2o/AkAYqJ+zZo1gVgR3YYSFXXr1qUJ4YkTJ6pvvvkm8Li4d0CkYbdu3TwnjxctWqS2bNmS1WUfeOABdcstt9AxHDRSqEwqlSpVUj179qRAEbdBI/gF2Rs++ugjt82u61Bq5ze/+U1mW5i3/zM7R/yAbA0IgkGJFjSMGzd2ezt69CiVCeGMJNDD3r17U9AIAmJQQgQBJ14NGVieeOIJ2vzKK6+o06dPe+0ay/qOHTuqzp07q7A6IQ1PkBCdEaBh5RR03qDtKFvFZbiQlQZ+CDaPQAVu8BHt2rWjRXsZLt7u1qNslLMMl9t+ca8LgyfomvCLCKxCwBlaIWw+aEx+29PATxhe0oCDeSiLeBi7V58Wu0mTnnnJOpv1OvEixW7C4AjiSCdegsaK7dLsRhoeLw510rMwdpMmXsLg8eKF1+vAjxQcLNMwfZr0LAyeoH100LOgMYbZrhOOMHaTJj0LgyeII5348RtrmnjxwxF2W1p4SROeMPaSJj0LgyeIH530TBoeP9mnSc/8cITdljY9SwM/ZcleWM/SwAuPNY5eJ7uJA08azxEYOFLWI2uk4+/atau64447KADk1VdfpYlxv8gpKAwmJZs1a0b6zqUokpQTggh48hjZUvbu3as2b95MmLIZF8qKIFsGghx44jvfmUac44NQUbYGf5UrVyYZo3wNsowgC8mJEyc839x38oYAH+cEcj4zjdivj88ItOCSNATE+gcsq1evVp988gmVswF+3OwHDBhAckfQyPTp09WOHTt8cSJoCRHUaAhc2r17t+/+TjlHWQYWZEcJyqAhDY+dVy+5RcnIEua8XtcLux7j6tu3L+kJynCNHz8+o3d+1+dAIRyIjDaF9gNh8Tn3g+8CXg4aSSLTiJ9cneOVwo8UHMyPNDyMy6tPi90YXtYFZjDKxv946UPU9WWFn7TYC/MojRdpeJgnZ2/07Oe8f6/JxV+mjR+nfvFy2nCUFftPKz88bmdv9Mz4s1z8LeuTsX+9vwcwT159WvyA0TOjZ3H4Ky874PVGz/TWs7LCT1r8srGbopdidZ/fYJ6k9YGBIzTjZv6JlQACJVq2bKkwqTp27FhfnHCqyAqBAAs0TO5PmzaNAjR8D0zBRpSCcQaNFCrTiJd4qlatqhD8waWDvPZzW69LVB6CX5B5BKWNMPm+f/9+de7cucyQsR0BIOiRJQKZRrZu3ZrZ7vUBGSQQaYn22muvUVkcr30LuV4anjCy09F2MG7c3IYNG5YpwwX/hkw3Qa1GjRpUvgs2hDJcCExKQ9PF5sPKSgo/UnAwb9LwMC6vPi12Y3hZr+bPn6/t82ZZ4Sct9sL2Lo0XaXiYJ2dv9MwpEb2W08aPl/TShqOs2D/zlTZ+eNzOPm04jJ7p+bxpeNGTF6e9ey2nxQ8YPTN65qXDca43eqa3npUVftLil9n2ygovjDdt/PC4JfbQPQQUol1iBRBYgTE/0wJ6TOTyMu1g7Yhl7I69OBKRDrAtm+3pkc/dd9+t8HfgwAE1btw4T36d2StQEmLKlCklggBYH9LGPya++/TpU5xpZNIkdejw4dTq969RqsKRdQATLmi62S8yokD+aHPnzqWyGkH6gxsIAgJq1qxJQTV///vf6Xgd9M8NDw3O+uc1PieeURaetPhXRCLbbWeSZTtHLNvRYfwIiMPY0BYsWKDWrl0bqP/gYvDgwZkyXBMmTMiU4fLijy5g/UtyO8btlWlEh/FhDE75SOEHZakeffRREjPpmVXuLUj/f2UFBDr1DJmydPDPLVG+zoaHy9c5+eNlAMfzgRceEoz1j/cP8u+F3O76hoF1r7yI59xfnnd1Gb+XvfiNz82f7bWe3YL0M+ntl/r4s0LqRzbff8oCP272wsE8bC+68WP8c/r8s7H/4vuln3/Pxj/FaZ9p5McNv5s/W6Dp/Z/Hb57P9Hw+Y37c7n/m+1mxP3OTD3ycn/zyvT1N/Bj7T5/9s/663W90fX72+j7jZ79u3ze5jH2S9s3y9/p9JU327yd/xpem5zNp38/8+EmT/bO9lgV+0mj/xj8voBgF+HY01lc/+2P/mPTvm6m+vvW7eGDgSBEl5r9UCTRo0EANHDiQDBCTpPv27SsF9fLLL1c9e/ZU9erVo22Y4Jo6dSqVHSm1c8pW6JotIaoYcQN0lqfhLyZRz5mv41j3cP4tW7aoGTNmhLrUPffcozp06ED7zp49W/3nP/8JdVy+d5KGJ0heutvOvffeSyWf8LCAMlynT5/2hYSHegQAcBmuVatWqSVLlvgeo8PGNNm8XV5S+MkHjtq1a1OmpkqVKlFQJ+7L9kxNdjnG/TkfeOIeYxznS5vdGF7wdUvflg9+kvQDTknnYi9J4pDGSz7wOLlOcjkXPUty3PngJUm78ZJlLvzohCcXHF6yKcR6o2fBzwFGz3LXRKNnwXqWu5SzP4PhJZgXneyfGU7b/SYfesay0KnPhRejZ7kzafRMb38mnZ9c7D937Y9+Bum8sGRy4UdH/8y40twHBo5QhA5nGjF90RvBwuTQvXt31bZtW3Xy5EkqFYJyIuC9YsWKqnXr1goOCm8Uo+3atYsm+M+ePVsc4ZVSeaDkjrM8DcqpZCLXfHBBNrVq1VIoZwNZ6GAnzqww69evV/PmzSPewowPzgBZhsLgD3O+oPMgGOn6669XP/30k/rLX/5CE/tB50X0KzjDue3llYKOK8R2aXj8+HPNNHLkiBZ2wONGthFE5UJPxowZ46vXCBpxK8NltwfYe926ddXGjRu1wekWwY6sF/ZxszycfdJ4pPATJw4EZ+J+27BhQ/Lb/O/UqVMKQXLbt2/31eM4/FyceDCepPXMqfdYTqPdGF5+yXjo8VyWtJ7FyY8OfsBuN1HtRQcc0niJE49u/jmqnumAI05edLAbu/3zc0VUfnTDExWH0bPiTGxu+sF6kmsflR+jZ/HxY/yZns+bhhdvXnSzf/aDUf1ZkvebOPUsSRx+96movBg90/M+Y/QsPl7YbiT7gaj2r4OeSeYF8oX+ReVHV//MuNLeBwaO8MSF6eVKABFdnGYeE46YaIXRYpIUPTekrP/ggw9KpAfibWnro2RLgCwQ6IBsF5ik4IZgE5RZccvWwvvku48alVe+fHl1++23q6ZNmxLfmKAEjuXLl6tvv/02r8MePnw4lZvZvXu3QomToNa4cWMqxwGsZ86codJKx48fzxyG0jUIFLjqqqtIR4Hjq6++ooCozE55/CANj5eootiO17nyud5ZhsvrWgi4QqaRJk2a0C5chuv8+fOZQ8qVK6fAb5UqVdTq1avVihUrlH17tWrV1HfffZfZvxAfoto8xqYDHin8xIED9xacp2PHjpl7Lh4uEVTHQZtYhl+G7uWzxYGHx6eDnvFYuE+r3RhemMHSvQ56Fgc/OvkBlnIUe9EJhzRe4sDD3OpgNzyWKHrGx+qAIw5edLIbli33UfjREU8UHCwDo2csifz1UfgxehY/H8afecs0ST9geCnNi472z6OM4s/42LTrmQ44eAzOPgovRs+cUsx9OQ5/xqNI0l54DM4+7XomlZ8ovDC3OuiZVF5YxlH40dk/My4JfWDgSNojY8z4w0UgIrLr/vvvp8wjTsU+evSo+vDDD9XWrVu1edM+F16jZBqpXr06Zbq48sorneKhZQTcLFu2TK1cuZIm/nIZH5xfNsdHzTTSpk0b1bVrV8os4wSFCUsECa1duzZveP70pz+pypUr00To0qVLfa+DoBGU4AHWCxcuqOnTp6sdO3aQnFDOAeVrbr75Zqq9ZceCABNkXUEASbZyzXZ/aXjc8OeSaQSZFOBLNmzYkJV+u40jjH0ga8PAX8pwjR8/XnEmJfv5atSooXr16lWqDBf0xr4frgd7QXYmrP/xxx/Vpk2bqEd0KwKvYCvI9uE8Lh/LUSNx7XJLGo8UfnLFgS8hyHYD20KD70VwEuwEwUjwb126dCH/hu3Tpk3La+aRXPE49T1pPbOPJ812Y3jxfy5KWs9y5Uc3PwC7iWIvuuGQxkuueOz+UJfnmih6phuOXHnRzW7s8o3Cj454ouCwy0EHezF6VvI5wOhZSXk49TXqstEzf7km9bxpeCnJi472z7+zpPl+k6ueOf1OUvbiHAeWo/Bi9Cw/v28aPdPbn0nkJ4r9O/1I0v5MIi+53Dd19s+MS0ofGDiCiQrTyo4E4IwwSV+nTh2FbA54+37Lli0isoyAxSjZEjApjMnlChUqkCIcO3ZMrVu3Tu3Zs0chY0e7du3ovNj4zjvvUCkL2rEA/6JE5eGYBx54QN10002ZESI7B3jGzREyQtYONEyEY0I8Hw2BILgWJkjnzJnjeQmUp0EZGIwbjvdf//pXRsaYtEfpGmR84AYsmPhHVhhev2TJErVq1SreJS+9NDxOIUWxHT7H1VdfrQYNGkT6Bb6ROQGlsdAQPASf8+WXX/LusfUPPvhgpgzXrFmzMlmBYMteZbjOnTvneX0EJ3HwiHMnlIaaP38+6ahzW5zLUWze6/pJ45HCT1QcuH/07dtXNWjQgChC9iroKe4xzga9w5cVZIUaNWqU8tNT57HZLkfF43WdpPUM45JgN4YXLw0rWp+0nkXlR0c/EMVedMQBzZDESy54iqyk9P8k7SaKnpVGULQmSRwYgTQ9A6Yo/OjoB6LgAH63ZvTMTSq5rYvCj9Gz/H7fNP7MX6eT8gOGlyJedLR/1pgo/oyPdfZp0zPn+Hk5KRx8ffRReDF6plQ+f9+M6s/svNo/Gz2zSyP3z5L4iWL/XhJMWs8k8cIyjsKPzv6ZcUnqAwNHpETIGBzhMo9IllOUTCMIWkDQCCIUIZuPPvqIMovgrXB7BCIyXqCEDQIWRo8eTW+K27fnQ65RMo3gGExUNmrUiPwYJu9RZmfbtm0ZPHAKd911F5VOAM5x48apw4cPZ7bHheuGG25QPXr0oDfsx44dS6Vx7HKCzDt16qTuvPNOujayurz77rsUNIL9mBvcaNCQEWfhwoXqxIkTxBWwAgd4wbETJkxQe/fujR0Hy0MaHsaFPpdMI3wee4QussYcOXKEyr0g+AdcI1sPAnx4/zh6nHfIkCFUhilMGS6MK+i6dhykeNY/BJIhyArXCDo+1+033nijevjhh+nScVw3STxS+ImCAxHS/fv3V/Xr1ycukUFp5syZZBN2P8j6UrFiRTVy5Eja980331S7du0iP8fb4+yj4Am6fpJ6BnlKsBvDS/BzbJJ6FoUfXf1AtvaiKw74JUm8RMWjq3/OVs90xRGVF53tJsp9U1c8Rs/0fd40eub9XGOeZ0q+kR3k/4O2S/AD0p5nJNk/619Z1TPG79Un6c+MnnnfZ5ivJPiJ4s94vF59EjigXzyebO1f1+dm4JHET7a8MJ9efZJ6JokXlm+2/OhsN3Z/wPgk9IGBIzRLYf4ZCaRcAjVr1lTDhg2jGyDKTEyePJmCIfxgtWrVijJdwEgQQDF79uxMpgvncXDgzz77LGW4WLx4MZUacO4T5zLG1K9fP9WkSRM6bZhoYHawyP6AhmwyM2bMoHIbtML2D85t8ODBlHlk48aNlEnFtjm2jyjRADn/8MMPmQAWzkBx2223KS4NdP78eRoDgkPQrrvuOgo6gRwwWY+AEUyiuzXOBILgFwT15LNJwwNZ5ZJpxClrrwjdsDbpPF+YZQQWdevWzbMM16JFiyjbTphzYR+c76mnnlK1a9emQ8LYXthzh90P9Q1hJ3FkOEkajxR+ssHh9N8I3EPQCAKX/NqLL76o4McLcY/JBo/fmHlb0nqGcUiwG8MLa5R7n7SeZcOP7n4grL3ojgOaIomXbPG4W0rJtUnaTVg9Kzli96UkcWBE0vQMmMLyo7sfCIsDmIOa0bMgCWW/PSw/Rs/ym2nEzlxZ9md2OXh9TsoPlGVedLd/1pWw/oz39+vToGd+4+dtSeHg66MPy4vRs/xmGrFzko0/sx/n9dnomZdkoq2XxE9Y+w8jqaT1TBIvLO+w/KTFPzMuKT3kjjlitEtatmxpBcj8TAvoMSHLy7SDtSOWsTv24sgZOsC2bLYb+eioH52s7BW33nqrmjxpkjpky6Dhpr/NrrlGPfrooxRogqCRt956q/gNb+sAN3yPP/64atasmfr6669pEjDf9gE8cLAImMAEMpqXfeLmgrfbUYoIjScqgY0bj5ftt2PHjgqZVJDB4/XXX8/YO2/n/b2OD7MdKaYwLi6Ng4lTBOHY29GjRynA5dtvvyV8La0sML2t8jRwXhg/gl+2b9/uOb5GjRqp3/3ud3TKP//5z+rM6dOu/PF4c8EXBQ8yp6DcDuOZaeHZZsPDsuDxNbbwPGHDc9rCg8bbcxm/m/4ga0vnzp0pwGiSZTtHLNtx0/+w13dG6CJohM5rZSBxu74Tf1R80AOUxKlzxRXqmFWGC9lnNm/eTPe5sPKDbiJjDwdsse3RuFN4f3TDs8DyJRdtkfNxyT9IP6Tw0wjl3qyAPpR7Q8kZLvcGHWH9vs8KZLr99ttJtMgcMm3aNAoa4e1u9lW1alU1YsQIsnOU9kLJJ+zPLUi+UbfjngF9Zzx7rYDDzVZZM8YT5vq4/zjthjP0hDke+0Qdfz7Of6kPnqj+KVt8bC9XWP4MZQXZn/2M53abvPzw62T/2eLn/e343PBwcB3vrxM/9913X2k/YD3T+PFXtUoVNeK55zJ+4LPPPiMRFBofXw8XR2Cmlz+z82P3b17+LN/8GP9c+vuLFP9s7D/Z32fc/Bl/x2R/4bTvKpY/e87mzzZY/szP/zmP9/IvfL18bNfh/s/4wjyfpck/h+E3Tc/Pueifmz/T+ftZWbF/9k868GPsX+/vz7nYP3+/1+H5jL9vlqXfz9z82YUUfj+DDvLzgt/91c2fFfr7c5jvZ+x/2T6c+HR6Pgtj/2l6PiuL/Dj1i/XPzV50fj6z//7C/sCun254Cm3/fv7JOf6y5p+d+L38H+tnXrZb81yBgSOsVKY3EpAggcsuu4yyW/hhqVWrlnryySfpzW4EMkyfPl2hjEBQGzRoEJUd2GJNqiGYoRANJUQQNGF3EG7X5UwY2Ib9Mb6gt9tRIqZLly4UnPHyyy8HXsPtumHWwQl1spWk4WPOnj1LmVs+/fRTKt+A9Q0aNFADBgzIBPSAm507d/Ihrj2yliBDBNrEiRPVnj17XPeLa6U0PJDLHXfcQXqDrC25tjgzmOQ6lmyOx5d3ZK/hoJEkMo1kM96gfQ2eIAnlZzv0H3qEdvDgQSqhhYxKQQ0BXPCTaMicFIct0sny/M/oWZ4FHPH0hpeIgovpMCl+QAoOplUaHsbl1UvxA2nDIU3PpOGRYi/SeJGGx+iZ+V7jpQNxrJdmL9LwBHGctucaLzxpw2H0bH0sGY299CFf642e5Uuy8Zw3bfx4oZaCg/GlDU9Z88/Mkw59YOAIRbhYESamL65dxpFappenF4i4+/3vf68QPAKdnzVrFmUlCNL/atWqqT/+8Y8U1bt06VK1YsWK4gjfhO0Hk/733nsv+RsETUyZMoUCMYL0d8iQIQplbfbt26f++c9/5h0P3jyrV6+eqlGjBk2oHjhwQCF4hMeJ7UOHDqUSHciGhNIOCNLh7V493oRFRC/aq6++SpkzgviMY7s0PF7yzWY9Ap369OmTKRlVItNIwnbih8MtEpczJvgdF4ce5eP8Bk8y93Nw+fTTT9P9BcEiY8aMoawkQXpSvXp19cwzz1AafPjwCRMmBPq9fOhN0Did242eJaNnTh6cy4aXZHmR4gek4GD7kIaHcXn1UvxA2nBI0zNpeKTYizRepOExema+10CnvfQg1/XS7EUaniB+0/Zc44UnbTiMnq1Tafx90+hZsr9reNk/r08bPzxuZy8FB+NKG56y5p+ZJ136wMARmm01/4wEyogEOMsG4H700Udq0aJFoZCjrE3z5s2p7AXeBkdZFR0a0oE/++yzNOH4/fff05vqXNrEb3xNmzZVKL2DhiCYZcuW0eck/+EtfUQZos2dO1dxina/MeGGOGzYMFWzZk2aoB01apTf7gXdJg1PkPDAHUrygBOUp5k8eXIqsiakLRI3iAeDJ0hC+duOslQInEJbuHChWrNmTeDFfv3rXytks6pbty79wIisSSh/o3szeqYnQ4aX5HmR4gek4GCNkIaHcbn1UvxAGnFI0zNpeKTYizRepOExema+17jpQFzrpNmLNDx+PKfxucYNTxpxGD2bT781ufGp6zqjZ7oyUzSuNPLjJlEpOBhbGvGUJf/MPOnUBwaO6BLhYsahdyShBH7KlStHtZYrVKhAGS/eeOMNKtOC6DY/fG3btlXdu3cnu96wYYOaM2eO7/5B54tz+0MPPaRuvvlmGg8yjaCsS9D5K1WqRJk9kEXlzJkz6m9/+5tCsEnQcfncXr9+fZo8hZCRZQQlasJcr3Pnzqpjx47EzezZs9Xnn38e6jg/vsNcN+h4aXiC8JpMI3r471wii6+44gp11VVXKfiH/fv30589I1AcdhGkR87tacPTtWtXKvsEHJz9yE9ueEBDgFmzZs3Ih61cuVIho5VdDoYX//uzn3ztcvTbLxc98ztv2OvHtV8uOKTpWZK85MMPJIFHCg62L2l4GJezz8UPJKFnzvHzci44kvRn0vQsH3iS5If1i/tc9CxJe8kHL9LwGD3L/flZmp6x3Tv7XPxAEnqWD16SwME8SMPDuJx9LnqWpH+OE4fRs9z9spMP57LRs5+VND3Tyf5Z33LRsyT54fFzLwVHHHiS1LN8PAckiYf5SEsfGDhCsxXmn5FAGZBAw4YN1e9+9ztC+u9//5uCDIJgIzNH3759FQzp5MmTauzYsZRNwX4cJlqR8eLIkSMUiGHfls/PcIQvvPCCQiDM1q1bKdgi6Hq4Mfbv319BFmhhM3sEnTfX7T169FDXX389BfL89a9/LSVjt/O3bNlS9e7dmwJFDh48qMaNG0cTrm77FnqdNDx+8iuLmUZg71deeaX66quv/ERT0G1RI4sRLIIHNfYJPOhTp04pBGPt2LGDVxW0TyMeZNyBX4I/wr3Cr8EXw38hkxUa5Dxt2rSMDzO8+Ekvvm1R9Qwj0MkPRMUhTc904CVOP5AkHik4IEM0aXiKUJX8H9UP4CzGn5WUZdQlaXoWJx7d7jdptpc4edHB/uPEY/QsqvcqfVycvOigZ6URKsraiyD+Jk2a0Ob169er+fOD38xPUs/i5CVJHMyHNDyMy96n+X4TBw6jZ3Yp5u+z0bPkf9eM059BU3T6fsaaG1XPdPADjAG9FByMKSoeHJ+0npUFu2GedOwDA0fSEgFjxqnHG+1p5qFdu3bq/vvvJztFcAICQfzw4AskgkbggH/66Sc1adIktXfvXgpUwEluuOEG1aFDB1WrVq2M7R86dIiCMXg/v/PnGgEH544yNWjvv/+++vTTT33xYKISZRS4HMwXX3yh3n333RIZOi6//HLVqlUrVa9ePTrXvn37aHL8u+++K7Ff3LiGDx9ON6vdu3eTnIPO36hRI9WvXz/iBllTMEl74sQJX/y5yjub4+PGkxQvQTyUxUwj5cuXV+C3SpUqavXq1VTq6dy5cxn7qF69OuliNvoSJOeg7VEipfFwcPfdd1PGHowVDdeBr0P5FF5evny5wp/BE/yGCOSJvwMHDmQC2dzk5nyoR2kaZIyCHhleguUcZA9ht0exG+ZTJz8QBYc0PdOJlzj8gA54pOBgfyAND+PiPoof0EHPePzcR8Ghkz+Tpmdx4NGJn1z0TCd7iYMXaXiMnsX//CxNz9j+uU/r/SYOXnSyF2l4WL+4j6JnOvnnXHAYPYvfLzMfzt7omR6/a8bhz3S0f9a3KHqmkx+QhiMXPDrpmXS7YZ507WGj0Ae0S6w3Ya1x/kwL6C9evEifzT8jgbIgAQRM4I0C6P3LL7+sLly44AkbE+KIesMEH/Z/++231ebNm2n/GjVq0JviyDjg1rD/smXL1KpVq9w2x7YOE9fPPfccnQ9BLQi68GrAATzAhYZ9p06dSpPEWK5YsaLq0qWLuummm2jiEuu4ITBj3rx5auPGjbwq9v75559XlStXpkl4yM6vIaDn0UcfpcltcDhjxgy1ffv2Eocgg8KxY8fUZ599VmJ9oRbiwpM0L37yyiXTSJL8OCftw75ZZJcFykOhfBVurj/++KPatGkTlXuCT0CWorVr16qFCxfaD8nb5yh4EBiCbBfsDxAsgiAYlOJCQB3rHXCiIRNGoTKPpBlPgwYN1MCBA+meMWHCBIXAO2dDEFjPnj0pOA/bEGQIX4yyQIYXvfXMyaUOfiDN9uKUJ5aj4HGeJ2lecvUDuuCRgoPlKQ0P40IvwW6i4tDtvilNz3LFoxs/UfUMx9mbuc/YpZH7Z6Nn7jI0euYulzjWRrlv6uLPpNmLNDx2/YyiZ/bj8TlpP4AxRMGhi71g/GhGz4rk4PXf6JmXZLJbn6ueOa+mAy88Jgl+AFik4MiFFz6W+6T1TLLdsIx17gMDRziQBCA44ghxJggv4WVsQ+Nls93IJ436gcAETOhDj6dPn07lXdz0v401WfrAgw8qRFMiCOS9995TX375JR2HoAVMuKI8DBqCE9atW0eBGFiHrCac0eOdd94pUcoiH/aDwBEEkJTIOGKNy84PxoVAi0aNGmHINJk5efJkdf78ecowgOwiyERSrVo12s77IGAE2VR4/ZIlS9TKlStVPuwfmV0gNwR6zJ0zp8T4eVCQH8o69OrVi2724A4y5oAWlm/9q69WgwYPJr4wEY5AFEyGY/tll12mmjRurL74hU83/uPA95gNzxwLDxqPz35+NzybrAAd8IdUbgj2YfnjHJiIdvKyePFiClJyO3++8F1jcQWdgY0gcAKBSyjVhOvZ8bldP0l+3CKkOR1ttvJr06ZNJngE3NgbfMKCBQvIf/D6bM/P+/sd74ZngZVe9yJ4sPTdTf4VrIwpfa1sPXg4Q0OWpJkzZ6rjx4+X4g/BMcCJsjWjRo2i4IYgfnPZ7oYniB9kfkD2IS88dvm54bFvD6O/Qfi6W/eOtrfcQj4HcuXgEQTjtG7dWt17772ZjC67du2iexEyjcBPww86ceAeg8Z84vrdH3oow8vf//53ylRi3273/3Sw4/hst+PLFcbGaZzD6LcXnuMWHuf1H7LhGWXhOWvL4BPH+HEOu3wu9cETxK9ze5AfCNLfXPC52UtZs38v+QXxkm//DF/Ttm3bjB/Yv38/+deKlp23tsryOf0AAmARPObUL8YXFo/X8Xb9d9ofruG1/UHLn91i82f7rWcQHO/lz2ZYz9Z+9huII+D+lSu+qP7ZSz5BePJp/3x/j+KfGQ/rFy8H4QnyL7nw4+bPguSn2/2f8Uuxf9YPLzy4z6PMqJc/05EfKfd/2L/xz9Pp+dfreTPJ52c3fxbVfwb65V++b7L/YX/O9svLUbdHtX+v64fF43U848llexR+dPv+HNX+vewl6e9nUZ/PvPAkaf/m+exnpeP9H99fvPxZ0Pcz3ezf7v/c/FnQ87P9ePvvX0n65yjPZ7raf1T/7HV/C+Qlz9+f8fwghR83ewl6PjP2r2jO0/x+5j6/EvX5mu098eOteaPAwBG+aZjeSKAsSOCee+6h8jI//PCDQvAEJru54YdwPExamXloFbJZ/Otf/1Jff/01LWM93hTHzQYPWB999BEFJTgzlyBzx5133kkT/KNHj6aJA75G3H379u3pBztMOIwbN44CWezXaNSokXr44YdV1apVafWePXvUW2+9RRMUWGEPXMDy1q1bKQgFk8ho+EEaaaOAB0E0Xm/R0845/MMPjz169KAMKMBh5wWnhSPr1KkTjQMOFmNBmR0OGnFe2h4xCX5wPgTKIEgG/CEAZunSpc7DYlvOFY8uvLgJJJdMI3y+pPhBeSnYA1qUTCM8fu7tOHhdHOflcwX12eLBmx/9+/dX9evXp1Mji8isWbPoh1e3a+HL2MiRI2kTSqns3LnTbbfY1knAA5852Apcq1u3LvmpgwcP0qQsluG7uK1Zs0Z98MEHtI/hRW89Y868+qT8gAR7scs0Wzz2Y90+J8ULxhLFD7hhsK9LAo8UHCxHaXiAS4rdZItD5/umND2LgkdXfrLVM/YdXn0SfpnHEoUXPtarTxseo2fzS7wk4MVrLuul6Rlkka0f0FHPovCiIw7WTWl4ougZy8KrT8o/S7AXlqnRM5aEd2/0zFs2YbdE0bOgcyfFC49Lih+QgiMqL3ycV5+knkm0Gy8567Y+MHCEIgytyQzTF7+hnYn8MXIRpxfgFpN6yOaA8gxffPGF+v7771XDhg3V1VamCjgrNASWoDzNf//7X5rsQ9AIMl3AoHAcspCgPIWb3eAczz77LGWKWLRoEQWYuO0Xh55hrI8//jiVx0AmiuXLl6sDBw6oOnXq0NvhmOTnyUoEwCDYAuPHePD2O4I1gAmBGCitgRIbbuNCiR+c6/DhwwrBMPnAg+warVq1ItkjSwfKz1CGECvLy2233aa4NBACQMDNtm3bfMfhFZlbKkNGnuw8Kh7deLHrA0qbeGYayVKOSfGDACToFUfM2vFlq9cIQho6dKiqXbs2TLFEJG4u581mHGHxYKzIzMFZI2A/yIiBwCq/67300kuqXLlyqlRmmyz5DisPCXgg6/vvv58yDpBi2P4dPXpUffjhh5mMV4aXoudPXfUsjN4m6Qck2Ivd/4TFozsvGF82fkBnPFJwsJ5JwwNcUuwmLA5wqPvzjDQ9ywaP7vyE1TOd/bJUf2b0zP33SMglye+b2fCSBrvJ5r6psz/LhhedcUTxZ2nAk42e6W43Ye+baeBFmt0YPVM0L6Db75rZ6Jnu9s/jk+IHpODIlhfe36+H3prnzbIXHxEYOGKbzzAfjQTKhATwFj0yiyBIwa1hQnXu3LkUUILt9slyBF2gzE3Qm/f4QbNZs2aUrQRv9OezVapUiTKh8ISw81qYGEZ2DWRI4daiRQsqucOBMHjQQqCGV2tkZS554oknaPMrr7yiTp8+7bVr5PXgBUEwCOpBw7hx47I3TLpirM6MJPZ97J+dEZMIGkGmGQTA5LtFwaMjL3Y5dezYUXXu3JnK08QhxyT5seOK8hkBYgioYrsrZKaRKOPt1q0bldLCsSiRMm3aNLIxv3OhDNaIESMomAzBXCj9pEtLAx4EJDa2SmMhkA9ZnL755hu1ZcsWCtRjOaYBB481TC8NTxDmtPgBw8t6xWl7gziNe3sYPxB0TR30TAoOlrU0PIwrl14HPQsz/jT5M2l6FgZPmvgJo29e++hkL2F48cLB69OGx+gZCi8UtknTszDSS4OeheElDTiYD2l4GFcuvU7+2Q+H0TNFZeR1/f3MjztsM3oWJKHst4fxZ0FnTQsvjCNNfoDH7NZLweGGzW2dTnpWFu3GjZNCrQsMHOEIX9O7R/gbuciVy4033qiuu+46Kl+CUi+oQb9u3TqFci7Me82aNdVTTz1Fb9wjkAFlXhA0wtu9+kGDBlE5CEwSona9135xrYdDQdka/FWuXJn8CzAhywiykJw4cSKTSQROeMCAARSUwYEwKFnhF3mIMguIPESbOHGi2r17t+/+UXFhDAhMQGkcfOYGLKtXr1affPJJJmOK33j5+vagn0JlGrGPKxs8OvPC8kQPbhBcheAd+3o77rDrk+Yn7Did+yGgqW/fvpmgEfiNODKYOK8T1zLkjPGioXTK+PHjQ9kRBwrhOGQaOnToUF7sPlucUvBIwcH8ScPDuLz6tPgBw4ve/tlLv3h9WvSMx+vVS8HB+AyeZL4fGn+mtz8rK/wY+0/G/tn/Gj27qMX3MeYjbJ82u5GiZ1JwsJ5Jw8O4vPq02I00XqTh8dIvXm/0LNnnGubB2aeFFx63FLuRgoN5CerTpmdlDU8Q3ly3BwaOYELINCMBI4HSEkDEHYIlatWqRRPkKI+CQIygVr16dfWHP/yBvtAj08fKlSuDDol1e9WqVRVqmB47dozGbT85gkqACT3K0yB7x9atW+27uH5u166dQsQl2muvvUblZFx3jGklxofMI5AlJqsR1HPu3Lmszo7SOigXg5tgITONuA0yCE9aeHHDFnWdTvxkg0GnSNww48ZDwLBhw8iPoczT2LFjFTL3BLUaNWqop59+mt48QDAdAsZ0aFLwSMHBOiEND+Py6tPiBwwvyWUa8dKdbNanRc+CMEnBwTgNHpZEYXvjz/T2Z2WFH2P/hbV759WMnhU+04iTgyjLabMbKXomBQfrnDQ8jMurT4vdSONFGh4v/eL1Rs9YEnr1aeGFpSbFbqTgYF6C+rTpWVnDE4Q3ju3QebwIjnZJy5YtrUCUoi8b6DFxnGtkijlez8hEw0vuvHTo0EF16dKFjAfZLhYvXhzKXlC+onnz5mRf//jHP2iSVhc+MDZM2KOhHA/KawRlisCNBBPPyL6CYJRRo0aFkkPQefO5HRGiffr0yQSNTJo0KZYMGfnisazwwvJLGz887jRG4lr3fbIF2DyyoqxduzbQfmHzgwcPVsg0BOwTJkxQe/fuDTyO5ZTPXgoeKTjYj0vDw7jc+jT5AcPLAnoWc+Mxn34qjuulSc/88ErBwfpi8OT+/cpPX1jObr3xZ3r7s7LAj7H/5Oyf/YbRs/TVXk+j3UjRMyk4ypL98/NPmuzG6Nkl9LKVrr+fsf249UbPkn+uSTsvPH4pfkAKDubFr0+T/fvhSON9MwwexpXvPjBwBJNJphkJGAmUlAAydjz33HOqQoUKVNrhjTfeUChVE9T+//a+/NeqIt+++NoOKLSg0AzKKD4BAVtpaVtFQEVNy0sAEYVWAxp9Dr8YTXz2X/CSlo79EpIWSEQUCOD0DAg0KrQCLy2gaLf6BEGCzCqoNIMM0t+96vK5d99993T2cHZV3bWTe+vsudb6rM+nap/9OVVXXXWVGjNmjD5s48aNavHixUmn1G1/z5491ZQpU/T9ZAqdNDe/8cYbFZJosLzxxhvq448/TnNaZcfYNpJFa7GLCMI2+0i9bc3EHT16tJ7CCp2NadOmqaNHjwqk0BKdx7vuukv169dP71+zZo1auXJl6LFVbHQFTxk4OnfurEdqOvfcc9WePXvUrl27ah6pKatNy8CTtS5lnmdbHCjDLlXqLMq2eeziGp4ojqrYnscuVdQ36Z558FBnSewm7y8jniXftf5H2Koz1+2Txy71V1HyHW3FQ50l29akI6izJmtU0Q9wzV/KwFOFXZpUEf7JNr9xzS5l4Am3dLVb8+isCr8pwy5V4Eiyeh67JF27zP1l2KfM+kZduwwcrumMeKLUY+f2xMQRvEySpTHjxduArbLO/Q0MCB8YwIX8uK2Pnr16NSZZIFnio48+avSHKPvjReukSZMUnO7gwYN6SojDhw+L+ySeX7a+xo8frwYPHqxOnjypnn32Wf0COcn/+/fvr6d7Qd327t2rMXnDDhir/7CRLL7++mttg7L5zXr9cePGqSFDhjTZ5ciRRH77e6NIYBqeZnZBXPJsE6XPrPUTARdxvo32AX503uHbffv21XRs2NA0x3wSPxipp1u3burTTz+txD4YeQdZ0/DfGTNmxOoDSSMTJkzQIyYB19atW9W8efOajTTSycPT1YcnCX/R+4N44q4fhme+h+cU/MSLafCXquwTxCH1CfNf6C9ol/nz5+uRFIAf03rhAaeX1275l0OHDqn/+Z//0XaMuz7Oybs/Co/Ux3/9M2LwCH5MEYcRbz755BN9Cf/5VfW/wjLyly5d2sw/pP5SX8EveD718NSz/i3s4lUo6v5h/uL3/+7du0fqDP2kLVu2xMaXstqnrPE5DR6xn9gzyr5F7od/RLU3SfevOj776xfmL8s8f/HH3yh+xV9c8H8T4nOYPm20TzCeRekHeMPimSntv9gjrP5Z/T9NPPP7Z9LzX5b9LtsnzF9Mb//D9CXtfxgeW+JzUGfiT2H6Tuo/gyNpb+rdPwuzT1b/h7+a1P5L/MjaP8P5VT2fiZ5a6MwzmPiP4IMNo9obU/rPLXCcfv6N0l/U86b4l/hLVf2zKDxSP7Ef8CX5P/tn+Z//he+gXcL0Jf6T1D9L05+Ju37QP7Eepg9cQ+of3B/EE9zvv38YHr//4/4uxecq7dPCLqfjWZh96P/R+vbrN4t/RJ1fpH1wD2lv6t0/a4HDq4vEr2B8SfL/NP4Spl/gj4pP2Iclz/6s/bM0eBpql69+teLL2n9Og6cK+9SKP0qfqfzbi6OJiSNiVJZkgAw0MTBs2DB122236Q3//d//rX744YemnSGf+vTpo7/oRwBGYgamRsGvvf0LXh7jV+BVLY899ph+CN++fbuuX1I9/Jh+/PFHNWvWLPXdd981noYHeryQxkMXpr0CXjxEImmmiiXrSBZV43DdLqKFrPaR86sq4dOYSkiSRjC9k3xZnFSns846S8G+7du3V5ju6r333lMnTpxoPO38889PjC2NB2f8MGLECIU/xB74cNSCUZYw0ojg3LFjh5o7d26z+hJPFHu1by/CLnhYwHWGDx+uHxxQC3QO0QbBnrL+7rvvau3pDSX9KwKPVM0EnUldpLQ1DhRhF5N0JvaQMotdXMMjXJjkN1nsYiIOqVMWPNSZsFdcWUQ8k9qY5C9SJ9t15qp9sthFbOqKzkzCQ52JNZpK6qyJi6I+FaEzE/oBReAQTk3QWRF4TLCLcBosbW1vXLNLEXjEtib4jdRFyiw6M8FvirCLCTjEDsEyi13kGiborAj7mICnCByu6Yx4RJlulomJIzoDRTL1WDb/5Sj5aLV8YGQE/OITCRH/9V//pV/EIViG+Yu8EEdDj+NfeeUV9X//939NGYDeefg1+DXXXKOnfPjf//1ffVzU9cra/uSTT6p27drpF4irVq1qVr8gLiSN4GU5Xj5iip4FCxboX63jOEyDgOlrrrzySp2Z5g+dSDB58803dQJJWTjCrhs2ksU333wTai853xQcLttFdJXFPmIn+M7+/fsVpn6S69WrDP1F3rLa5piX6auA54g3msxnn32mS2S3XnLJJWr9+vVqmXdNwVt0iVEopnhTVCE2Pf/882r37t0teOzYsaO64447FOqEZefOnXqkEfhzsD7EE94OBHlKWs9rFzwc4ldh8C0sSBZBchL8BImOiG833XSTjtPYjxFK9IgQEe1YUn2T9ufFE7x+1Trz18fmOJDXLqbpLK9dXMPj5wPtogl+Y7O/BPnEehY81Fk5/Zq88SxoXxP8RfqzLujMRftksYtrOjMND3UW/hxiezxzTWem9ANc85e8eEyxS1DvWLe5vXHNLnnxBO1re3w2xW/y2sUUHEF92O7/gievfeQ6UlblN3lxuKYz4in3e3TRe5VlYuIIXihwIQNkoDkD5513nnriiSf0S9OFCxeqTZs2NT/g9BqSJ37729/qjj5ezGJaABm60X8CjhszZoy+HhIa3n//ff1yD/e5+uqr9Wgdy5cv959S+GckgiDJBS8WFy9eHHl9TE+DaW2QCIMvNF977TU9zQZOwItlTJGCkRJkwUgjeMGMocRk+8qVK9WaNWvkkFJLSdzBwxZezL/44otKpqeJurFJOFy1i3CfxT5y7sUXX6ymTp2q/Qa6xcgJMqINkqCQ4PSPf/xDDi+0zJPxHayI3/+D+2oZwSR4btr122+/XQ0dOlRz9/LLLzeOhnTOOeeoQYMG6cQ2GaFi27ZtCjHv+PHjkZcnnkhqatqR1S5nn322Tmzs2bOnvt++ffsU7HrgwIEW90e7g4cuTFszffr0WLu2OLnGDVnxRN2map2hXi7Egax2MVVnWe3iGh4T/cYFf/HzmgUPdaZUmf2arPHMb1f/Z1vbGVN15pJ9svi/X1v+z7bqzI/B/7lqPNSZ3xpNn6u2C2rikt9k1Zlp8TkrjiZlNf9Utc6y4jHNLn5WXfAb1+ySFY/frv7PVfsN6pJFZ6b5TVa7mIbDr40sdvGf7/9ctc6y2sePwf+5KjxZcbimM+Kpz/fofs1X8TkxcUR+6cLSm/uupF/i8rrhv4wwnRf8Wvv6669Xhw8f1lO7fPvtt42/1McL13//93/XU7XAsTEqBxIsMJpAFC5/xmQwGCDhYcaMGfrFbtT5ebcPGTJEjRs3Tv8yfebMmcqPB/6PxIuRI0eq6667TmNAIszrr7+uk0awHwklGJUAHRssmzdvVkh2+f777zUvePF8ww03aM5w7uzZs/XIBXnrHXd+lpEsTMPhol2kPclin6C9/X4DP0PiFaZ7QfIPNLt69Wo9kk/wvLzrV1xxhRo7dqzW+oYNG/SoINB11uv6ceiLev+KuG6a+oCnBx54QHXt2lWPPLJ3716NA+s4X5Z169apFStW6HiWdF3iyd+uZbELMr7vuece1aNHD222rVu3qkWLFmmfEL/zl23btlVPPfWUPvall15SSAzy70+ycy37s+BJun6VOgNPLsSBLHYxWWdZ7OIaHlP9xgV/8cfHWvFQZ+X3a7LEM1P9Rerlks5csk+tdhF7RpXsz+TvN0t8ps6iv7ekzqrVmYn9AJf8BfE1Cx4T7SLxLMtzTVQ7I9uriAOu2SULHuE/qqzCLnl0ZqLfZLGLiTjy2CVKX7K9Sp1lsY/UO6qsAk8WHK7pjHjq9z26Px5E+UGZ2xMTR+SFEUsyQAaaMwDnwWgHF110kU62+Pvf/67++c9/KgxdhZEQJIECiSWYnmb79u3NLxCyFpYxmXaUjJDL1bwJUxsMHDhQJ8MsWbJEffHFF3r6GozcMGzYMNWtWzd9TbyYf/XVV3VyCDZcfvnlOukEnODlORJG8NI7bJERNDDqx3PPPRd2SCHbsoxkYSIOkOGSXcS4Wewj5wbLML/BMWX7DuY3xMgmS5cu1S/bg/WqZR3x4sEHH1SdO3fWp5X5i9yweuH+t956qx55JLgfUwG9/fbbkSMrBY/HOvGEsVL7tlrsgvg7efJk1bdvX30jxG8kjSChKm55+umnFTr+77zzjp7OJu7YvPtqwZPmXlXrDHV0IQ7UYhcbdFaLXVzDY7rfuOAvfo7T4qHOyh1pxG+TWuKZ/7yozza1MzbozCX7pPX/KG35t9ukM3+9oz5XjYc6C7dM1XZBrVzym1p0ZnJ8rgVHuLKab61aZ7XgMdkuflZd8BvX7FILHr8toz5X7TeoV1qdmew3tdjFZBx+naS1i/+cqM9V66wW+0Rh8G+vCk8tOFzTGfE0KLCe36P7NV/vz7A3ElOwtBkwYICXyPIvvYISL4Crzmzh/aN/MVBmRhF5T8c7hmbCyCJItghb8OIOCRiYPiOtvTDs1a9+9St9OXnxjakG0p6f5zjgwa/VkQyDBS8ckU3pX/ASGVNVyIgkXtzQiQ0IJidPntQvKoE7qh5IQrnvvvv0Jf/whz/oaWzK0BtGNxk1apROHpgzZ44eiSLuPqbiAI8u2UV0Uat95LyoMphpDN9JY/eo69VzO3xs0qRJjS/86zXSSJg/9O7dW0/x06VLFz21yc6dO9Xnn3+u+wJhx4fxRDzp2o+0fOK4NHa55ZZb1DXXXKNjK0YOmT9/vo7hcff5+c9/rh5//HEdrzFFGaZ8ijs+zN5Zjk+DJ+m6JumsCF5MwJPGLjbpLI1dXMMer1tUAABAAElEQVTTWvzGBH9Joy+xB3WWf2S2WvgG72nimdgnqqTOiu/PiB1bo31c0VkUDtlukt9QZ00jbJhkF4kDeUqT8KTRmQ39gDQ4xM+jStqlvHYzj7+IvUywTxqd2eAvYo80eIT/qNIEuwieNKUN9kljFxtwpLFHlK6C203SWRr7BOsfXDcBTxocrumMeP6lqvoevch4EPSnqPXExBH/C2N+JgNkIJwBTCeC0SowPcaxY8fU7t279RzeaUYZ8V+xyFEY/Net5TOCwkjflDRyLnCtXbtWvf/++3raA2zv2bOnuvfee3VyCZJGFixYoL788ks5JbTEqCUYWQHLCy+8oL766qvQ44rYeO2116otW7YojG4St5iOA3V3yS5ii7T2kePjShN8J65+UfuQqYxReGSUiHqPNBJVr6zbiScrc/nOg/6hIyyYaghTgWFkqKQFCVyI91gwAlRSrNQHGvCPOqvGCK7pzDU8SapwxW9sw0GdfVDIyGxJ+i56P3VWNKPFXs82+0ShdwWH4CMeYcKsknap1h6tpR9gm85ai11E/bbYh3Yxu9/sin1cwSH+nVTa4v9JOGS/LXhc0xnxNCjQ1u/RxX9qKRMTR5BxIktjZou3AVtlnfsbGBA+MIAL+aE+RA9p/eOyyy5Td955p07C8I80kvZ8uV9R+mvfvr1OhOnQoYPCiCd79uzRSTFyfex/6KGH9FQdGJ0IUyJs2rRJyX6pT7D++EU8psTAMm3aND0tDj7L8UnnF70fU438x3/8RxMObzSVTZs3N9YHdcMSrF8QB2ymM/S8Y8v0//Zefbt7I8J07NhRvyBGktLx48cb64f9D/nwYHSYzT48UfxF4dHgQ/ALHybsR+dl4sSJjb7jH2nEhPqhDsKXn//QDGlv2ptTXrsrx5tc/2D/IAyPTOMjePz4g+fXw39quX8YnmUG2gcduUceeUR16tRJJ4vMmDFDjxaTxC9i+6OPPqqnFUICH5JNauEn6fpl7T/DS7aKHKHHc5gy428Z/OChNwqPSf4PHw7qDCOQYYnzb7RVLXTmXassfaS1fxieAx6epPM7xOApQx9J/KbdHxbPbIzPtvk/47NvpBFPrEn+Zcr+MH8xsf0X/w+LZzbH56T2wTb7AE9Y+2BL+x9Vf9Gf7LctPgfrH4w/rtgnzF9sbP/FPmF4GJ+rb19t83/2z8zsn4X1Z/h8Vr1/S/x1xT70fzP9P6n/L/tt6Z+F+YvNz2dheGyOz2F4bLaP+EfS801N+73vbREvwZU+L2yqGr2H/8gAGSiVAbz4DiaNmP7Lb/y6HfXGgul4Pvzww0SO8KD/8MMPqwsvvFC/2Jw+fXriOWUf4AoO4ck1PIIrqrTRd4AFnV3YiiONRFm22u022ad///46cQqMLV++XK1bty6RvDPPPFNNnTpVde3aVb/Ax+hPO3bsSDyv6gNssksarmzC45rOXMMTpzebdOYaDupsabMksTj7mrLPRn+hzqizqv3HRr+J48wVPK7gEFvZiKc1xGfaRRRqZmmTfVqDv4hKbLKL1NkV+7iCQ+wSV9qoM1fwuKYz4lHK1u/R43wqaV9i4oj+BcHpXwYiw4TrTb8EJx/UQxH+EDXSiMn66tGjh37piACDUUYwRU2a+o4aNUoNHz5cx6U33nhDffTRR6nOK4LnsPq5gkP4cQ2P4IoqL7300siRRsLsHXWdem8P/aXUsmUKI/eYXO8onvLg+cUvfqEu8kbQOffcc/UUXxhBB9NiVcmDbXhuvvlmhWmfYB+M4oTRj+L4Q8cPSUv9+vXTsXj16tVq1apVxvfv8tgljo8oXZe9PQ+eKvzGNZ2VgacKuyTpNI/OTPIbW3FQZ8n9GpP8hjoz+3sOW+0TjNN5cJjkL4IrDx6T2pki8JhkH9rFjHhWRj/AJL/Jo7Mq/cV1uxQRz6rQGe2S3G+uwi6ipzLsU0UcKANHlXYR+wTLPPGZePK/7yxDZ1X4i+iKeOz8Hl3sl7VMTBzRbxX4jwyQgVIYsHW0hHHjxqnBgwerkydPqj/96U/6RWUSQd6IRmrChAn6hebevXvVrFmzKv8Vois4hHvX8AiusNJW38mT8Y2Rerp166Y++eSTMEoq2ZYVD5JF0PHs1atXs3ofOnRIIals69atzbbXa8VGPBitCvEVcXXmzJmxVOHhEXEYCYtYwPP8+fObxWKXdAaMLuGp0m9c01mReKq0CzQetWSNZ7ieSX5jMw7qLEqdSieNmtQPoM4wEHnDYpL/S51sto9gQJkVB9sZP4vlfXbFPllxgFn6f7H6KrIfYJp9surMhHjmsl1EwVntU6XOaBexXnhZdXwu0j5VxoEicVTpL+Eqadhqo/+7hqdInVXpL2IX4rHve3SxXZ4yMXEka0YKzzMjw512MNcOeUYawfQCeElYlX0fe+wx/aXC9u3b1Zw5cxLr0bt3bzV58mT9ZdmPP/6oX25+//33jeddcMEFauDAgap79+56265du/TL8R9++CH2l/N58buCQ3hwDY/gCpZZRxqpSmdS/zwZ32effbaCfdu3b6/Wrl2r3nvvPXX8+PFG/+jQoYOCT9UzMzwLHnQ6RowYoUceQl2xgB8koWHYN1l/9913Ff6IJznTHXzib8+ePY0JeWG8BR8eMTXN3Llzm+nIFZ0JflfwmOA3rumsCDwm2EXal2CZJT6b6De246DOWj6Hmeg31FlTf9KVdtOVeGaiv0h7Y7vfCA4ps+Ax0T5ZcJjoL3nsYhKeIvoBJuHJYxeT/MVVu+Sxjwk6o11a9ptNsIvoqgj7mBAHisBhkl3EPlKyH9DkR1U+1xShMxP8RXRFPH31+wpbvkcXu+UtoUHEOyxtvF+setdr+LUJSgybz4UMkIHiGcgzWsLo0aPVNddco6cWwAtk8dniaxl9xSeeeEK1a9dOv7z+61//Gn2gt6dv377qrrvu0i+Ff/rpJ7Vw4UK1ZcsWfU7btm3VTTfdpH75y18qBCP/ggSTN998U3366af+zYV+dgWHkOIaHsHlL7P4TtU6Q/2DL+0/+OADtXRpbXOyX3nllWrMmDG60cZ0JJ999pk6evSoHoXkkksuUevXr1fLly/301Xa5yx4kBiC0S6Q+IMFySKIYRs3blQHDx5UYifgxIKRMOo18ojNeHr27KmmTJmi+2yzZ89WSLwLLkiaGj9+vE7Ow76dO3eqefPm6WmBgsfarjPX8JjiN67pLC8eU+wS1DvWs8Sz4HVMiAMu4KDOmivLRL+hzo41N5K3ZoL/S6VcsA+wZMFhor+4Zpc8eEy0TxadCQdS0v+FiWLKvP2AYC1MsE8WnZnmLy7aRbSSxT5yrpRV6Yx2EQuEl1XZRWqT1z6mxIG8OIQPKau2i9QDpc3+78chn23Gk1dnpviL2IJ47PkeXWxWRJmYOIKX0pJJxzL5l7/ki3pJ8pM8I41AX1dddVXjy+NvvvlGvf/++3qkASRyXH311fql4bJly0r127vvvlvhBf6HH36olixZ0jhySFD/wHrHHXfozgv2vfrqq/plNz5jqC0MdXX++ec3xjK88ETCSKdOnRq3r1y5Uq1evboUPK7gEN5dwyO4pMwy0ogJOsuT8R2MJ37/b3Sc0x82bNig4PtI+gyeV+R6FjznnHOOmjRpkkJnE8u+ffvUokWL1HfffdcifiA5Bjgxbc306dN1ckOR9Rc9SekCHnA2dOhQnYADXnfv3q15RTLOoEGDFBIO8eCBZdu2bTqB79ixY5E6sVVnUTqxFY9pfuOazrLiMc0uft1niWf+8yUuoqzSb1zBAR6ps4bnZxP9hjpjPwD9orL7z1l0ZqK/SPuQBY+J7UwePCbah3aJ/h6yyv5Mnn6AiX6TRWcm+otrdskTz0zTWdZ+s2k4/PXJ4jf+88W+KG2NZ6bFAeos/v1m1TqD/l3wm6w6M81fJB4Rz8LY9xMm+I2/vRC75SkTE0fwMM2FDJCBYhjIMlpC2J39Ga3B/RiJYMaMGeqf//xncFdh64MHD1bjxo3TIwbMmjVLIYHFvyCwjBw5Ul133XX6xSReZL/++uuNo4f4E0pw3ubNm9Vf/vIX/RIZ68gqxTBYOB/nRv2KHsfmWVzBIRy4hkdwocziO6bobMiQIWrs2LEaTpaRRvw84HOY/xdx3eB9otZrxYOEhXvuuUf16NFDXxKjiLz88st6ipSwe6CT/NRTT+ldmErlyy+/DDussG0u4EHMvP/++xWmMUPMxFRm6BxiHaUs69atUytWrNDHyLao0jadReGQ7bbhMdFvXNNZFjwm2kU0jrLWeOY/N+xzVX7jCg5wSp0pnbhoYj+gtesszOf926ryf6mDK/apFQfbGaX4XCNekL6sVWdJV6b/JzGUfn+WfkDS1auyT606MzmeuWQX0Uut9pHzosoqdEa7RFmjaXsVdpG7Z7GPiXEgCw7hIKqs0i6okwv+7+fWBTxZdGaiv4hdiCd5Zpaq44DYqqgyMXEEmSqyNGaoeBuwVda5v4EB4QOvaMgP9SF6EP/AKBoPP/ywzppEcseLL76of3Uv++X4tPoJy2TT150zR+37+uvS/ROjhQwcOFAdPnxYLV68WE8/c95556m+ffqoYb/+tZ4+A9hOnDihXnnlFfXFF1/oX8APuvxyNc6bMgHBBy84MUICfukVhl9G0Pjaw/Pcc881jEzgXbNI/8LUGZd7dWqBw5tiZ9iwYS1xeEkuuD9+xY/kGcGBKUIwVUiY/TDSAhIfNI4//7nQ+oNjLMLfnR6egTXgQdIOlig8eqf3T64PfHf78PzZw4PFv79I++Da4G7ixImNvjPH0ziSleLapyg8VdkHiVTwj2Xe9DSnvHZV+AI+LLIeVr/g/jO8JIGHHnpIde7cWZ/b7JeS3pai+Q/eH9cXPDLdTlT94R+/+93v9JRVuA7iAEbEwLRVcfb7/e9/r8466yz19ttv6+lsoq6Pa2LJu1/wJNnnDA/P5Bg8UfYTPO94eNZ40/NIfRtqn7/+uB4y8m+77TY98ohcV8r9+/ert956S23etCm1PnC9FjorQL+16LPRLjKSjgeolvMFv/ATxJOkX//5OnM7x/1xfhBP1PXB/eTJk1v6jTe9Uxz+Rp29845as2aNXL5Rb1H6FD2m2Y/4k6gztJMpnx/w8Bm0i4ycJABqqV8cP2H4cP+0eEz2f78+RWdF6LtK+wgO0UOY/aCRNPoIi2dF8FPL/RN1djo+m+z/fv9Ka58oPJiuLo6/YDzLY3+/f4he9M29f404Cmjfqu6fuRafYSNX7NOI43R/RvQnehR9R/nLTwa0/1n8P4hP1v34q47PrvTPYB/RWRHtW5Xtv+hD8CQ9n4n/hOlL4j/jc1N/SfgVvtLwl8X/o+KZKe0/8KOOaftnwlccf2HxrAj9+vmPu39YPKvVvv7rh+EpIr7gHsJnWP1qeT4LOz94/ar937X4XIt9TH5+pv9730t7zhIVX6ryf7//NvYDEvrPEk9wLhZZ9+MLw1OP+FzL85kN35+74v/+/mFiP8Ci7zej9A+8soT5R+h+r58ETeJ4fd0BAwZ4xzVcCCVe6nIhA2SgOAYwigamk0HSCJII8i6//e1v1a9+9St9GUlGKeK6aeqFUQHwQhhTgWDBi2A0xP4FLyvxklhGJOnfv79CogYCDx4YsW/Lli3+U5p97t27t7rvvvv0tmeeeUYdPXq02f4iVlzBIVy4hge4hg8frkaNGqXSatxEnYl98pbopCGhqq+X2ISlnr/Iy1L3W2+9Vf3aSyTDgilS5s+fr2NF3LXat2+vHn/8cd05QVLaxo0b4w6v6z4b8PTq1Uv18RL4unTpokdx2rFjh9rkvZCspU9nm86SRGAbHuqs6aEmybZF7U/jNzbYpSg+cB3b/CYKu0k4WqPOWovf2KazKH+R7SbhkTrlKW3B01r8RWxpi12kvq3FPrbZRewTVZqEJ00/IAqHbDcJj9QprLTJX1qTXcJsFbbNBJ3RLi0tY4JdpFZp7GNDHEiDQzBHlSbZJaqOtWwnnlrYSndsGp3Z4C+CtjXiEexRpWt+A5yJiSNIIGnMRPEyTLhOPqiHfH7Qrl07dejQodx+hWk4MOoHkjXkhfq+fftyX7cW++JYvNCXKWkkeB47dkyPEvC3v/1NJ4ggbqBRuffee3V9kTSyYMEChSkr4u6HaRbwS2QsL7zwgtq+fXvs8Vnjkys4BL9reIALGsNoFTLSSJRuTNaZ2CdrCV/H6DWSNNJspBED2+dLL71U1xf+i6lTnn/++cZ4EGU/bJdEIZyHkYbqHdei7OManiictuksCodstw0PdXaqlHZe9JC1bC12EX5s8xupd7C0DYdrOnMNT1Bfsm6bzqTeUSXxVPN9T2vxF9GdbTprLfaxzS6ip6iSeBjP4r53iNJNrdups2p0lmQn2qVau7DdNPN7DfrNBj3yPX5YV4/2IYlv2e+av7iGR+wUVbrW3gjOxMQRvLjhQgbIgFkMYOqOYNJIvUYaCWMCyTAYeaRDhw76Je/u3bvV8ePHGw/FfiSAoETjjJFGZIqUxoNCPmCkAmRcYvnjH/+op5MJOaywTa7gEEJcwyO4okpbdBZV/7jttmWuonOBqbkwRRemq5o5c6bCCERJS8eOHdUjjzyif+n+1Vdf6YSxpHPqsd81PFGc2aazKByy3TY81Fn9RxoRrcSVrcUuwoFtfiP1Dpa24XBNZ67hCepL1m3TmdQ7qiSeKGbK3d5a/EVYtE1nrcU+ttlF9BRVEk8UM+Vuby3+IixSZ8KEWSXtUq09WkscoM6q1VnS3W2xj2v+4hoeV3SWhCNsP2yJBCssbcKmqpEME5bVZmqSf/IvmZAmjDRSqx4xtQaSXbAsWbJET68heKJKNPB48XzhhReqAwcOqOnTp1c+4pErOMR+xPMvnaBgms7EPlLamLnq9SfUxIkTtc8v8+ajXL9+faL/wufvv/9+hZGGgH327Nlq586diecJT2WWruEJi7s26iwMh+jARjzUWb4R3eL0ILrIUrYGuwgvNvpNmN1txOGazlzD44rOwnC45v824mkN/mKjXcRfWoN9bGw3xT5hJfFU9/1pa/AXm+NZmL+4hof+X53/i75aQxygzqrXmegtrLTJPq75i2t4wvTlWrspeIJlYuKIfuvDf2SADBjBgGkjjaQhpWfPnmrKlCn60E2bNqmFCxemOU3deOON6vrrr9fHvvHGG+rjjz9OdV5ZB7mCQ/ghngYmTNOZ2EfKPBnSnTt31iMBnXvuuWrPnj1q165dzUYCknuUUY4ePVr95je/0Ukf06ZNU0ePHo29DTr1d911l+rXr58+bs2aNWrlypWx59Rzp2t4gtzZqrMgDlnPg0euUUVJnVXBevI9XbeLMJDHb6psb6T+UubBIdeooixDZ1XaxTU8QU3k0VmVdgnikHXiESaUqsI+rvuLsJtHZ3KNKkrX7WOrXaK0kAdPFf4fhUO224anDH8RLkwqbbNLEnd58CRdu5778+Bwzf/ryXvwXmXEAZPsQ50FLW7Weh77VIHENX8pA08Vdkm6p206S8ITtj8xcSSYacJ1szPqaB937WPjSCPQ4/jx49XgwYPVyZMn1bPPPqtfICfptH///noqHmT17d27V09zgQCWdF6Z+13BIZmSxNNGmagzsQ/KrBnS3bt3V+io9erVq1m7f+jQIYUkrC1btpQ+lyNGG0GWMfx3xowZsfcDzgkTJijEOCxbt25V8+fP19NaCR+Y8gYjkXz66aeVxAHX8AivtuvMj0Pif1a/wfnUWXkjftAuTXPoVq2zIv3GhPbGj4c6a9CZCXYpst00AU8ROjMNR952k3iK+97BZX/JqzPX+mem+Y3N7aY/LufVmWl2sRlPkfHMBP+nzuKfD016rskaz1zzfxP8psg4YJp9qLOm/q9J/p+33azSb1zzlyLxVGmXsPbfZp3F4RFcwTIxcaTZWyeukAEyUAkDNo40IkQ99thjerqZ7du3qzlz5sjmyLJPnz5q0qRJegqRH3/8Uc2aNUt99913kcfXa4crOISvovFgSiEkClx00UX6hT9Gt/jkk0/UwYMH5Zallq7hyZK5ik7AiBEj1PDhwxvnoEOjj6StM888U/OP9XfffVe99957pdoD9cAfRjqBD0ctqBdGGunbt68+ZMeOHWru3LnqxIkTjaecddZZCvZt3769Wrt2ra67f//555+vfvjhh8bjy/jgGh7hyHadCQ4ps+CRc6kzYaL4knYxK54FLZzFPia1N4InCw451xX/N8kuRbSbJuERrWTRmYk4iEcY8OZrNqD/7Kq/CMtZ/EbOZXwWJoovbbdLkJEseEzw/yAOWbcVTxHxTDgwwf+lLlLaahepf7DMgkeuYZJ9suBwzf9NsksRccBE+1BnojKlTPJ/qVUW+8i5VeJxzV+KwGOCXaQOwdJWnQVxpFlPTBzBSyZZELR15om3AVtlnfsbGBA+2pAf6qNA/0DSCLL1kNV65MgR9aKXfLHv66+t8b8nn3xStWvXTr/sXbVqlYrzDySN3H333fol908//aQWLFigR0eAh4l/YSSF/fv3q40bN+p4hH1YZH/c9fPErxY4TsfDhrs3v38f7yV4EAdGUfDfX3B8+OGHdal/kJ8oPGH84aV+FJ62bduqm266SV155ZUKDYp/QeLPkiVL9CgRwfvLuhwv62H3xzFJ+6PwhF0/zj7nenhujMHzpofnE2/UC6lP2PXzto9n/OxnOnlKkik2bNigli1b1jACh3fDsOujk4tROy699FJdJSSLIMkC+kLyDqarETvhAIzogZFHZBE8WfkPno/RTqZ4U1SdOnVKPf/882r37t3N9I/jO3bsqO644w6FXxBg2blzp5rnJY38eOxYC36vuuoqNWbMGL0dcfCzzz7T8RDnXnLJJWr9+vWao6LqL3iEnyg8sj8KD5JgjvnwSP3S4vFf3x8/gvWTdbm+rMedH/pLiaVL1Smv3xd1/lleos+EO+9soTPEYyTvwH9uuvlmHQ9w73nz5ukRZOR6tdQPeGVJc34YnqUeHs1byv5rol3WrVPLli+P5Ccvvl6Y2m3q1Ea/QQIeFj/+jhdc0NJvPJ4Rb4P3T8KzzsOz3IcneH4t/EfpEw9VSAYNi2dpr5+Eg/6f/fksi32i2puNXnvzQ0x7U4a+xD/o/z8q0+IzpkSc6otnu714Fuy/XBATz5BYemdMe4P+582+9ma+Fwe3eH1tiZdp44scn0afLvTPBC/4ccX/pf2x2T5523/T/N/vf4zP5sXnrP7vjx+4RtX9M6kP4rfN/h/W/tgcn/O2/9AWFrFvos5Kfj7z2ycsni3j83P89zR1sA/9v8lfGryn+vic9/uzqOfNqr7fRDyi/7f8fs72+OyP73ieSMJT1vdnUf7ir1/c9+dR/lLV9zNReCQ+wZ/C8Jj8/Tn0gSVL/0z6M4I/SWdlf78p9fHrS/Chjo37PTslJo4IKJZkgAzUnwGbRxoRtpBwABx4sbh48WLZ3KLEtCGYPgVBGAHrtdde0wkH/gMvvvhi/SU0ghiuh5ETZEQLJKf08RJP/vGPf/hPKeyzKziEkCLw4KU9vtTHiA+y4EUnXmBiyDrZvnLlSrVmzRo5pJTSFTzQP7DIS9YPPvhAycvvKOLOPvts/WIWX9Jg2bdvn3r55ZfVgQMHWpyC5At0UjBtzfTp09Xx48dbHFPUhttvv10NHTpU+yjqIy/BzznnHDVo0CA9nY6MhLJt2za1cOHC2PogOUmSR4J1TMNT8Jxa113C45LOYMcseKLsT51FMVP7dtolOr5WrTNYM4t9TGxvsuCIUnPVdsnazphoF3DsEp4sOjPVLrAN8Shlmn1c8hdoDEsWnTWc2fI/43NLTrJuccku4CALHtP8329LF/BkjWd+Hvyfq/Z/1MUFu/g5zYLHf77/c5X2yYLDNf/328L/uUq7oB5Z44CJ9qHO/Mpq/rlqnaE2WezTHEXTWlV4XPIXsJkVT5Mlmn+qyi7+WrigMz+eNJ8TE0fwAtefacJ18kE9xM/xWBQ/l112mX4pj6xWPdLIiy/qF8JFXb9e1xkyZIgaN26cni5j5syZ6ttvv232C3DgGzlypLruuut0rMEIBa+//rpOGgmLN/7MPIxK8s033+hpLZDEgGutXr1aIVGhaHyu4BBe8uJBog9Gi0DDiWXz5s36l+vff/+9ti8SAm644QZ1/fXX61/Pz549W48oIfcvunQFzxVXXKHGjh2rOW020oiXLBXmD8gsvueee1SPHj30ORjZZtGiRdonwo7HL3SfeuopfexLL72kkLARdlwR9oE/PvDAA6pr165aA3v37tV+iXVcXxZkba9YsULBn5Pu6/d/OT8NT0nXTbPfJTwu6Qz6rRVPkr2ps/B4k8RbcD/tEs9jlTrL4jemtjetXWem2gXxIEu7aSqeWnVmKg6J08RjXv/ZJX/JqjM5L6qsst10yT61+n+UPWR7lXZxqT8jfNZqHxPbmyz+IvijSuos/rkiireo7bXqLOo6sr0q+9SKw0R/8X8fVyse4T+qrMouqE+WOGCqfWq1i6k4RCe14pHzosoqdZalHxCFQ7ZXgcclfwGPWfAI/1FlFXZxNT77cUXxje2JiSPyYoYlGSAD9WPgwgsvVA8//LAOtJI08rU3PY2tC6bQGDhwoDp8+LCeuuSLL77Q09dghJBhw4apbt26aWgnTpxQr776qk5CiMMalWlYNleu4BBus+K5/PLLdTIQGhAk+mCqA7y8D1tkJBDo97nnngs7pLBtruDBfIAYQSdppBHwP3ny5MbRSeBXSBpBAkbc8vTTT+v5KN955x09nU3csXn3IbHo1ltv1SOPBK+FKafefvtttWnTpuCuyHVc78EHH1SdO3fWx9RjpBF/ZVzC45LOYKO0ePz2jPpMnUUxU/t22iWas6p1hpqltY/p7U1aHNHWaNpTtV1qaWdMtwtYdQlPWp3ZYBfYhnj6ggZlUv/ZJX/R5Hr/0upMjo8rGZ/j2Kltn0t2AfK0eBifG3RSj+8DaolnadRbtf+3Zp2Zbp/W6v+m2wX1qyUOmB6fqbNoxdkUn6NRNO2pCo9L/gI2a8HTxH70p6rs4q9R2jjgPyfqswl4ouom2xGXkUCCpc2AAQO8hJOGOXtQ4oVg2gwUHseRSOIylKiP2vWBUTiuvvpq9aKlI4349YCh5jAqwkUXXaSDDV5sI/vQv+AlMqaqCI5I4r+OX0fBTEMkjcyZM0ePQOI/Lur8LNtdwSH8ZMHjtRMKCRpoPE6ePKkTFfCFaxSfSA667777tKn/8Ic/6Gls5P5Fl67hSeLnlltuUddcc43mFiOHzJ8/XyeNxJ3385//XD3++OPaXpg6ClM+xR0fZddat/fu3VtPJdWlSxc9hc7OnTvV559/ru+d9v6IGZMmTWpMlKnXSCNh9XMNT5w9bdJZHI4wO4YdT53V3l8J4zEt32mPo12qtUtriQO26cwmu6RpN23CExf3XMEh8Zl4/qXq3X9uTf4iOksqGZ+r7QdE2ccku8TFZak/45mZ8UzsE1VSZ2b6v9jLFvu45v/Cf1Rpkl1aU7+GOtugli1bpt8pp2mXo/Rb1XYT/MY1f0mDJ8neJtilSD3bgicxccT/YpefyQAZqC8D5513nh6lo753LeduCDYjfVPSyF2OHTumRz14//339fQasj2p/Ld/+7cWU/nUY1QWV3AIv7Xg6dmzp7r33nt10g+SRhYsWKC+/PJLuVRoidFkMEIElhdeeEF99dVXoccVtdE1PFG8QP8YzQULpoDBVEAYsSdpwfRB8EMsGAGmHj6jb5bzHzJxgbdv3776SvUeaSRn9Vucbgse6uyDxJF/WhjXoA226CyJMldwCE7b8LSWOEC7iEKrKV3RmSs4RAXE08CEaf1n1+wieosqGZ+jmKl2O+1SLf+tJQ5QZ9XqLOnuttintfiL2MsWu0h9XbGPKzjELkmlbTpzBQ91xu9pk7Rc1H68Y0PCDBaOOOIRkZThxP1mZzrTPubbp3379qp79+6qY8eO+oX3nj17FJJHasncu/TSS9XEiRMbp/Ipe6SRMF25gkN4T8KD/Q899JCeQgWjUWFKFEwxIudHlRgRA1OVYJk2bZrCyDBhfEadn3W7a3j8PODzI488ojp16qSTRWbMmKFH8UjitUOHDurRRx/Vw8UhgQfJJv7rJp1f1X5bMnHT8mMLHuqMv5QwIT7Y4i+u+b/gaS1xwDaduWYXV/C4gsNV/3fFPq7gEJ0llYzPZn6/Q7tUa5fWEgeos2p15kp8bi3+Ivai31TjN9QZvz+DBsQPyyqpM+qsHjoT/SYmjui3ffxHBsgAGTCEAWRW3nnnnY1JI5jKx5ZRE/wU2oYDoz2gzliWLFmiPvzwQz+c0M94YHn44YfVhRdeqBMbpk+fHnpcFRttxtO/f3+dOAXeli9frtatW5dI4ZlnnqmmTp2qunbtqjuyGP1lx44diedVfQAz2KuzAHW2VPtKdRbIfmdX/MYVHGJJG/G0hjhAu4hCqytd0ZkrOEQJxKOUif1n1+wiegsrGZ/DWKl+G+1SvQ1aQxygzqrXWVwNbLJPa/AXsZVNdpE6u2IfV3CIXeJKG3XmCh7qjN/Txmm56H2JiSPIMJGlMaPF24Ctss79DQwIHxjAhfxQH6IH+kdx/hE20ogkjQjfNvjfpV4CRuSIKQbGD0xRg6QDLJs+/1wtWLgwVfwfNWqUGj58uD7vjTfeUB999JEywT49fHg+9/As9OGJq18YHg3O+1dP/d08erS69tpr9UvtZ555Rh09ejT2/kjgQaJMv379dHVXr16tVq1a1eyleD3rn7Z9DP2lxNKl6pTXL5H6VsF/2voH+09heJZ6eCSTV5ceoLDr/+IXv1AXXXSROvfcc9Xu3bv1H0ZqkkX4iNNv3PXDzr/55psbdSajBcVdHx3KoM5Wrlypqxh2/SA/tdavqPPx0Dtp0qTGaZA2bGjKYC+T36Lqjzr6+T0jBk+c/ariP1h/0X+Yvyyj/9fN/0Wfo33tzTSvvTnia2/C/OP/xbQ3or+oeCb7Rc9h1xd9SP2i9JN2fx7/79y5s7r44oub4vKuXerY8eON/lhm/V2Lz2F44vgLa29Wee1N0fqoVV+u9c9c8/8y7BMVz+L0mzY+RekvzF/i4meYv7B/1mAhaW/i+IO9atnvun1s7Z+V4f9l6CdtfCgjPgueMuNXWnx5+mdV1r8M//+F19+8yNff3OX1N4/7+pu1xKe0/Ee1P3J+HvtU0X92zf+j7JMnPkf1Z+qhrzLsE4WnzPhQhv/Xg/9an5/y+H+Z/Et8ivKPqP22fX/mevsv9guLZyZ/fx6lL/GvMDxpv9+MimelxwdvBJ3ExBFxapZkgAyQgSoZsG2EjiiubMQxbtw4NXjwYHXy5En1pz/9SU83E4VPtg8YMEBNmDBBv0DZu3evmjVrVrNEBTmuitJ2PBhxB/yC15kzZ8ZSiM4J7HDZZZfp47Zu3armz5/fzBYYEaZbt27qk08+ib1WPXfiYQRJCH379tW3/eCD9HMYuoQHySJ4AO3Vq1cz+g8dOqSQjAV7lrVQZ/HMuqQzIDUJD/2/QXtV+r+ov8g4YAIewYUyq85MwFGkXcBF1f7vCh5XcEATWIrE45rfVImnSLvAzlX7P+oQXLLGZ1ynajwu24d2wdf/DQt1JkwUX1JnDTqrsp2Js2pW+1SJx+W4LLay0S5S9yLtQ50Jq+WUWXWG2lTdboYxYiOeIv3FNbtU6f9h+pJtWXVmAp7ExBFkzEjmMcvy56oi39Qb/ayln4WNNPLNN980/6V+HeaSy+ufWXFccMEFauDAgap79+4aM35lgJf8P/zwQ13i82OPPaY7edu3b1dz5sxJ5L13795q8uTJ+qXMjz/+qJMbvv/++8Tz8vKb9vyi8dTbPiNGjFD427NnT2NCTljcCHZOMDXN3Llzm/1C5eyzz1bgo3379mrt2rXqvffea7a/Q4cOCrYLu35avms9LjQTd9kyderUqcR6uIIHnTPYGCP2gHss4BHJWxg2Xdbfffddhb8y7EOdRfdHXNGZ6MYkPPT/f+ms/qr9X+J2EXHAhHgmeKTMojOTcBRhF5P83xU8ruAQPykCj2t+YwKeIuxikv+L3qTMEp9NwuOqfWiXphHFTOg3U2ctn9NcsYsJ7YzE42CZJQ6YgMdVfxH72GoXqX8R9qHOWr5HEX6LKrPozKT+WZAHW/EU4S+u2cUE/w/qS9az6MwkPKgL9IKljfcLZg9XQ3YrSryk4UIGyAAZqJIBG0foCOMrC462bduqm266Sf3yl7/UL5L810VCxptvvqk+/fRT/+ZSPj/xxBOqXbt2Oqngr3/9a+w9MELEXXfdpV9u//TTT3oamC1btjQ7ByMoHDhwQH344YfNttdrpSg8VdkHUwdNmTJFt9GzZ89WSCQKLkhmGT9+vE42wr6dO3eqefPmKf/0JnLOlVdeqcaMGaM7A0eOHFGfffaZnv4Go5Bccsklav369Wr58uVyeKllMNmllpFGpGK240FiCEaJQaIZFiSLIKln48aN6uDBg0p0B5xYMIJMGSOPUGea3sh/tussCMwEPPR/pdtOE/xf9JE3DpgSzwQPyiw6Mw1HXrv4+cDnqv3fFTyu4BB95MXjmt+YgievXcS+Ulbt/1IPlFnis/98fK4aj4v2oV2apgcVvVFnwkRxJXV2zLjnAL91s9iH7aafwXI+22wXYSRvu0mdCZPllVl0FqxN1e2mvz4248nrL34e8Nl2u5ji/0FesZ5FZ6bhSUwckQwZli0zmiVDi2X5mY3UX+vUX9YROkzTSxYcGJIKQ5Cdf/75je0PEgSQMNKpU6fG7ZijevXq1aWMOCA8YsoQJL4g0WPJkiWRI4dgOpQ77rhDN44499VXX9VJCHIdlD169FBTp07V9cWLcIyWICOnnHfeeXpqkr///e/G46naPkj0GDp0qE4kWLRokdq9e7e2C5IKBg0apDDvITocWLZt26YTeJA0EtVeXXXVVY3JI42CO/1hw4YNalnKET+irp9me5ZM3Kjr2ornnHPOUZMmTVJ4GMCyb98+Bft+9913LfwOGgBOTFszffp0nRQUxUfW7dRZfP/GVp1F6aFKPPT/fynT/F90kjUOmIgni85MxIH+VFa7iF2DZZX+7xIe2mWh7o+45jem4XFNZ4hHWeJzMI7JOuNZfP9VeEpT0i7mPj+7FAeos2PGPgcgTmSxD9vNdCPmponD/u9T/ce7YBfBkzWeUWdm6kzsGiyr7p+hPln8JohD1qvCk9VfpN7Bsioc/npksYtp/u8insTEkdPvjViQATJABurKQJYROupawZQ3y4LDn4CB22zevFn95S9/0S+PsY6sRQxPdt1118WOOoFji1gGDx6sxo0bp0c+mDVrlsI0Qf4FDcnIkSN1fdBQYrSq119/PXI0FH9GK0YlwfVOnDihR8dAZwGJMKtWrfLfotDPefGYYB9o4P7771ddu3bVfO/du1cnhWAdNpBl3bp1asWKFfoY2RZV+u0ix2QZ8UPOrbUcMmSIGjt2rD6tiPvahgeJPvfcc49OrgIJGEXk5Zdf1lMHhXGJTvJTTz2ld2EKoi+//DLssFzbqLNk+mzTWRKiqvDQ/83zf9FKljhgYjwDnlp1ZioOYMliF5wXt1Tl/6iTK3hcwSE6yYLHNb8xEU8Wu4hNo8oq/R91qjU+R+GQ7VXicck+tIsoKrykzsJ5qXVra9eZie2M34a12sdEPC7FZbGNC3YRLFnsQ50t1T8uEw7LKmvVWVI9qmw3UTcX8GTxF9fsYqL/+zmuVWem4klMHInKbOT21jkCBO1Ou/sz6MrSQ5YROupRr1rxZsGB0SKQpIHgjAQMTBGCqULC8MlIIF9//bV67rnnWoxIUGt9447H6CcDBw5Uhw8fVosXL1aYfkZGCBk2bJjCtCZYkADyyiuvqC+++CK2PlEZrZgqZc6cOTqZJK4+YXzUcnxWPCbZB0k2t912mx55xN9Bwef9+/ert956SycdpeUF13vooYdU586d9eXqNdKIv35IQIKuihjhxCY8qOvkyZP1iDsgH/6DkUaQWOXnJ6j73//+9+qss85S77zzjlqzZk1onIg7P3i9sHXqLL7dt0lnYfYN6qNKPPT/vjr2muT/oo9a4gCONTWeAU9anZmOA/5ci11M93+X8LRmu7jmNybjcU1ntcRnxjOl6vmclrbdpF3qaxeX2s3W7P8mtzPyHFCLfUzG05rbTZPtIjqrxT4m46kFhw3tZi3+Tzz16we0Zp2Z7P8Sz2rxG5PxJCaO6G8w+Y8MkAEyUEcGhg8frkaNGqWQRPDiiy8qJEbYuNSKo3///mrChAk6aeTkyZP6xTESNKKW3r17q/vuu0/vfuaZZ9TRo0ejDs29HaMb/O53v1OYogULXmijcfMvSFbAy+7giCT+Y/yfg5nG9bR3Fjym2qdXr16qT58+qkuXLnpUmh07dqhNmzalGmVE7IGMZSQi9e3b8OKyiBE/5NpVlLbhufXWW9Wvf/1rTRWmFpo/f772sTju2rdvrx5//HGdLIJkLkz9VOZCnbVk1zadtUTQfIsreGzDYYP/i1LSxAGb8AiusNImHGnsEobRv80kv3EFjys4RCdp8LjmNzbgSWMXsWFUaZL/R9Wxlu0m4aF9mixHuzRxUfQn6qyJUdt0ZkM708Ru8icb8LRGf7HBLqKuNPaxAU8aHII5qjQpnkXVsZbtxFMLW+mObY06s8H/01mv4SiT8SQmjiBDRpbGzDFvA7bKOvc3MCB8YJIA8kN9iB7oH9n8A9Ow4Fe3kjQifNrmX9dee60enQPJFDrzMCI+oLG/9957dTIGkkYWLFigp6qI0w+mJcEIEVheeOEFtX37dlUmP7ABEnpgG3yW5dixY2rt2rXqb3/7mzrpjTiSNv6FjsjiJQmlPT9v+5QGD0ZQwWKDfeL0Fec/6LxPmjSpMWnE/ws2sXHc+TjGpP1nxOAp0z+y8o/prMA/Fkw59Pzzz+tpoZL0LYlpOA8jDu3bt69U/8+KD/WDPpBoFtTZ0qUNw2uapJ+08ScMzzIPzymv3yx4gB2LrJuoP6lfGB4b7UP/b9KbFl+d9XdpTDyL039YPKui/uL/6J+0iMsp+jfDb7hB95NQ9z//+c+6D0v/bxPb/y2Dn7B4ZmN8tq1/5or/S/+L/bOm/kxc/M7bPyz6fFf8H+0I4mMYHvbPwE5Tf6cKfdoWn6V/I/EtyJ9t/edg/QVfmL+Y3P6H9Tfl+6e4/tENvv7mc15/c5/3HZocD26wyHo9/SMMT5rvB23rP9vm/+yfNfhD2PNmPf0jKv5K/AruD4tnNrb/gi8Mj8nxOal/GobHRvvY1v679nxmdHz2nkMSE0cauhz8TwbIABkgA2Ux0K5dO50AghLT02DUjs2bNyfeDiMUIDMRyx//+Ec9jUziSQUcgHpi5JEOHTrol9W7d+9Wx48fr+nKaOwxXQw6W/UcaSSskkl4bLNPGMaobXjo5UgjUeyUvx2dsIcfflh16tRJT/M0c+ZMPc1Q0p07duyoHnnkEQX7ffXVVzpxLOmcKvdTZ1Wyn3xvV+xjGw7X/N8VPK7gSPb8hiNs85skXK7gsQ2Ha37jGp4ov7FNZ1E4ZDvxCBNmlbSLWfYI1sYV+9iGw7V2xjU8QT+RdepMmKimpM6QhmHfYpvfJDHsCh7bcLjm/zbgQR2RiIqlzYABA7yEqoYghBIvMHWGlXcAS98vLsgH9eD7RXFjJjd1QV1k1AVe3CORAsuSJUsUpglJ0hUaeLxwvvDCC9WBAwfU9OnTreE/dKQRGZHFQD9y1T5hGdLLli3TbX+S/kzcbyMer9+lJk6cqH0f3K9fvz7Rj+H7999/v8KIQ+ibzZ49W+3cuTPxvKr6cTbaJU7fxGNmf9hGu7jm/67gcQVHXByT9sBGv4nD5QoeG3G45jeu4QnzGxt1FoaD8czMfhntYrZdXLOPjfHMtXbGNTxh7Q11Vn1co87sez9ro9+E+T/bTfp/nC5EH7WUNsSzxMQR/TaD/8gAGSADZKAUBnr27KmmTJmir71p0ya1cOHCVPe58cYb1fXXX6+PfeONN9THH3+c6ryqDzJppJE0XLhqnzyZxZ07d9Yjzpx77rlqz549ateuXTWPOJOG+1qOyYOnlvsUfezo0aPVb37zG530MW3aNHX06NHYW+Ch66677lL9+vXTx61Zs0atXLky9pwqd+axC3VWvuXy2Kf82qW/g604yvD/Kv2mDDzpVVDcka7gSGIkj99UqbMoXK7gsRVHGX5Tpc7KwBOl3Sq259FZFfVNumcePFXqLApXHjxR16xiex4ctEv5FnPFPnlwlM9y9B3KaGeq9Jsy8ESzV/89eXTmml1cw1N/NUXf0VadRSHKgyfqmlVuz4OnSr8JcmYrDtfamTLwFK2zxMSRWjJlis684fXsyySkXqrPwKPf2OU348ePV4MHD1YnT55Uzz77rH5xnORH/fv319O8wNZ79+5VmN4CS9J5Ve+3baQR8OWifbJmfHfv3l2hY9OrV69m/c5Dhw4pJC9t2bIlcaScMuJTVjywL6aIwcgdn376aSX+g9FGkGUMP54xY0Ysf8A5YcIEddlll2n+t27dqubPn99shJiq8fjtm9Uu1Fl9+hFZ7WOC3xShMxNwFOn/JvhNkXiqtI8rOPx+Aj7961n93wSd+XEILlfw2IyjSL8xQWdF4qkynhXpL6bhcM3/8+IxzT42xzP6TfT3aSbEZ799surMBH8psp0xwS5F4jHBPkXozDW7uIaHOmv+fAg+/LrPu25zfA7jISseE/zGj8dmHK61M0XiKUtniYkjzd4OcYUMkAEyQAYKZeCxxx7T081s375dzZkzJ/Haffr0UZMmTVLIEP3xxx/VrFmz1HfffZd4XtUH2DbSiPDlmn2yZBajkzlixAg1fPhw/SABbvAQgWSnM888U1OF9XfffVe99957Ql1dyix4pGJnnXWWgn3bt2+v1q5dq+t+4sQJ2a3OP/989cMPPzSul/EBvOIPI7fAl6MW8IyRRvr27asP2bFjh5o7d67y19cEPFL/LHahzoS98sss9pFa2a4zk3AU4f8m+U0ReEywjys4hMtgmcX/TdKZq3hst0sRfmOSzorAI1pluylMFF/a7jdBRrLgkWvYrjOT/F84ldIVu+TBY6J9bLdLEe2MSXYpAo9olPFMmMhfFmEX6iy/HZKukCWemWSXIL4seOQaJvm/1CkLHhPtYzuOIuKZ2NQEnRWBp2ydJSaO5M0w4/nFZuyRT/KJoEAduKODJ598UrVr106/tF61alVshi+SRu6++279sv6nn35SCxYsUBh1wHQ92DjSiPiZS/bJklmMzhRGuYANsSBZBEkWGzdu1EkVmK7mpptuUldeeaXejxEw6jXySBY8Ylcpr7rqKjVmzBjtd0eOHFGfffaZQols3UsuuUStX79eLVu2LNYv8/gfRm/BVFWnTp1Szz//vNq9e3cLf+7YsaO64447dJ1A8s6dO9W8efN04pjgkLJqPKhHFrtQZ+XqTPSR1T7+86F3W3VmGo68/m+a3+TFY4p9XMER5BPrjM//Uqb5jSt2yes3ptklL56g/7HdLP77C8azlt+H2Koz0/zf779ZdOY/35R+szyvZsFjon2y4DDNLnnbGdPskhePafZBfbLozDW7uIaHOiu+Pybti5RZ/MZEu+TBY5rfMJ6Z+f153nazHjpLTBzRb4L4jwyQATJABkphAIkgGI0DL+IXL14ceQ9MT4NpU5Ahig7Ma6+9pqfXiDzBkB22jjQi9LlinyyZxWeffbYe3aZnz56ajn379qmXX35ZHThwQOhpLJF8gS8rMW3N9OnT1fHjxxv3lfEhC56oeiDpRZJHgsd88MEHaunSpdrngvuKWr/99tvV0KFD1cGDBzW/u3bt0pc+55xz1KBBg/T0QDKyy7Zt29TChQtj+a0STxa7UGdK1UNnEFUW+0Tp3DadmYgDdcrq/6b6TVY8ptnHFRx+XrP4v6k6Ay5X8LiCAzbJ6jem6iwrHnARtrDdDGMl2zaX/AYMZMETxZxtOjPV/12yi2gli85MtE8WHMJBsKzSX1CXrO2MiXbJgydoF1mv0j5ZdOaaXVzDI7oKltRZkJHs61n8JupuVdpF6pQFj4l+4woO2CVruyk2DZZV6ywrnnrpLDFxBC8oZWnMAPM2YKusc38DA8JHG/JDfdA/GB9OjwyTFB8HDx6sE0IwksPMmTPVt99808x/kK07cuRIdd1112lOMTLB66+/rpNGTI/PoSONfP11M3xJ/FS93xX7XHHFFWrs2LGazg0bNuhRNKAlWYLtFzJX77nnHtWjRw99CEa2WbRokTrhJYSEtf9t27ZVTz31lD72pZdeUkhwKFOfUXiytr/+X+gJJ+BJkkaC/Mi6HCvrWe6PTvwDDzygunbtqkce2bt3r/Z1rOO6sqxbt06tWLFCYbQhuZ/sk3W5fxSeZV4SzCmvXyfHR52fdf8VQ4aosePG6cum4Q8JMffee29LnXlTBoXpp62XTPPUf/6nvj509uWXX+rPUl/BL+t58Q3x8Izz4cnLX6RdvFFt4I9F1z+Iv1b7BM8P1i8JT9L5Wfe3dv83OT6fkSOeBfUl+ojSWZnxGX2vmuOyV+Gw9lHiURQOjGplov+7Fp/P8tqbe2Lam6D+kLz5n772ZpvX3sTZN3i+6FfsH7U/Kp5Fne+a/0fhkQTkIH9R/c0ofoPny3oUv/79rbl/lsRPUjxLOj/v/lr7Z6b6v+ittfbPTPZ/aLTW+Cz2jNJ3pN+U/Hwm8TEKj+wP1t9U+9Tq/1H4BG+kXerQP8vS3zS5f5YFj6n2ifKXKP+O8hcTvj/L8nwWhYf9swYFSPxI0m/S/lrbf5P9H9/fuRSfgccV+0TFsyh90v+V0u9N6tA/Mzo+e+8jEhNHpFFkSQbIABkgA+UwgKlABg4cqA4fPqyWLFmivvjiCz19DaamGTZsmOrWrZu+8QnvZeqrr76qNm/eXE5FCryq7SON+KlwxT6YPw/TIsnLNj9G/2d0DCZPnqz69u2rN0OPSBpBwkLc8vTTT+sh4N955x09nU3csUXsS4snzb3wcuDBBx9UnTt31ofXawQIqRvuf+utt+qRR2SblPv371dvv/222rRpk2xKLKvEk9Yu1Fn9RhrxCyatffznRH22QWdRdfdvrxIH6lGL/9vgN7Xg8dsh6nNV9nEFh5/XtP5vg86AyxU8ruCATWrxGxt0Vgse4E9aqopnqFdanSVhwP4qcUj90uKxQWfAlBaP4I8rq7RPWhy0i9nPAabbJ63O4vxE9lXpL6gD7p/2ewDT7VIrHrFBXFmlfdLqzDW7uIYnTl+yjzoTJvKXaf0mzZ2qtIvULy0e0/3GFRywSy3tptgxrqxaZ7XgqbfOcD8kymFpM2DAAC+hCr+l8X4x5ZX49RPKxky607+g53r5c4KRd+qOftZ6/AxDTGF0h4suukjHX7ygR7a+f8HLY0xR8e2331oRl2+44QY1atQodeTIETVnzhz1DUZSsbQ9cdE+cfHllltuUddcc42WH0YOmT9/vk4aibPfz3/+c/X444/r/gKmXMLUS3HHx92/3ufB1yZNmtSYKOMfkaXe9ezdu7dCwliXLl30lEA7d+5Un3/+ueYyLS8m4YnjjzprGvknjqe0dq/3cbboLIkXk3Ck8X+b/CYNHhvs4wqOWuKMTTpLg8sVPDbhSOM3ruGxIZ6l8RfXcNiks9ZkH9rF7OeA1mIfPgeU9717mn6AK+2NTf6Sxi6u4aHO+D1tEf2rJB3Z5DdxfNiEI008S7Ib+wHx/YDExBH/i0t+JgNkgAyQgXIYQDAe6ZuSRu5y7NgxPXrD+++/rzDiiE3Ltddeq7Zs2aK+9qansX1x0T5hNsFIMXfffbfevoGVGAAAD6RJREFUhSlTZs+enUp3SBSCfrE899xz1tgcmb3AK6Or1HukEU1Ygf9swUOdfZA48k+Bsij8UrboLAm4bTjoN3b6DXWW5Inl7nfFb1zBIdZ2DY/giiptiwOu4KDOzGw3aRcz7SJ+31rsY1tcbi12ER3aYh/X7OIaHtFTVEmdRTFT7XZb7CIsueI3ruAQuySV1FkSQyp5qpqkzBzuj8/MIT/kJy6Tj/qgPoL6aN++verevbvq2LGjwov7PXv2KCSPBI/jejUj0rhsH2jqkUceUZ06ddLJIjNmzNCjXiTFqQ4dOqhHH31UDxf31Vdf6WQTG/RpUmZxEXzZgoc6M/sXhkn+bovOXMNBv7HTb2zzF9d05goeV3BIXHYNj+CKKm2LA67goM7MbDdpFzPtIn7fWuxjW1xuLXYRHdpiH9fs4hoe0VNUSZ2Z+T7GFruIrlzxG1dwiF2SSuosnf9zxJHk5BoeQQbIABkgA2TAeQb69++vJk6cqHEuX75crVu3LhHzmWeeqaZOnaq6du2qp1N54YUX1I4dOxLPq/oA2zKLk/iyCQ91tlT7SpJNTdxvk87i+LMRB/3GPr+hzuK8sD77XPEbV3CI1V3DI7jCShvjgCs4qDMz203axUy7iN+3BvvYGJdbg11EgzbZxzW7uIZHNBVWUmdhrFS/zSa7CFuu+I0rOMQucSV1FsdO832JiSNJGTrcny5DhzyRJ2TvUQfUAXVAPzA1Dtx8880K0wuhftOmTVNHjhyJHekGHQhM89KvXz/ds1i9erVatWqV8XHOtsziJL3Yhoc6OxXrV0n2rmq/bTqL4slWHPQbu/yGOjOjv++K37iCQ+Kya3gEV7C0NQ64goM6M7PdpF3MtIv4vev2sTUuu24X0Z9t9nHNLq7hEV0FS+rMjOc02+0i9XfFb1zBIXaJKm3zf8FRlX0SE0ea55lwjQyQATJABsgAGXCRgTvvvFMNGDBAT5E0c+bMWIjobE2YMEFddtll+ritW7eq+fPnGz+Sgo2ZxXGGsBEPdRZnUTP32aizMCZtxkG/CbOomduos38ZYxhX/MYVHCIM1/AILn9pcxxwBQd15rekOZ9pF3NsEVYTl+1jc1x22S6iQxvt45pdXMMj2vKX1JmfDXM+22gXYc8Vv3EFh9glrKTOav+eJjFxBJktsjT+Ut7bgK2yzv0NDAgfbcgP9UH/YHw4PcIM4yPjIxiwoX0YMWKEwt+ePXvUrFmzmiWB+Ov/M296Gow00rdvX21cTE0zd+5cdfz4cWVy+xeWWbx0acNwwX58tvRvwvAs8/Cc8vptgqfB+8zS34iRIxt1JglKUl+/foKdeq2zl15Sx0+cMBrfGT/7mZo0aVKjf2zY4JvL3DOILfqS/j/sEIXHRH1F8RvmLzb5P+OzF9cs8B/b/d+1+DzS197M8hJio+JDWHvzktfenPC1N1XqzzX/D8MTxi+mQ4zqb5rc/oS1Nzb0z4L+YXv774r/618ahrR/ttqH/g9Pa1jCnn+k/40jqtjvqn3YP+Pzs/jTafcrxL/C/CXs+rZ8fxaGh/2z6r+/ce35LNi+MT6bEZ/p/+yftWi/vPeaiYkjchJLMkAGyAAZIANkwF0GevbsqaZMmaJOnTqlZs+erXbt2tUC7AUXXKDGjx+vunfvrvft3LlTzZs3Tx07dqzFsSZtCL4U+uCDD5S8NDapnmnrYjMe6iytlas/zmad+dlzAQf9xm9RMz9TZ+b1A1zxG1dwiOe6hkdwoXQhDriCgzrzK9Ocz7SLObYIq4mL9nEhLrtoF9GfzfZxzS6u4RGNoaTO/GyY89lmuwiLrviNKzjELv6SOsv+PU1i4ojOcD/9y3nJ0GTZRv8SmzyQB/pH0y/b6Q/0B/qD/f4wZswYNXToUHXw4EG1aNEitXv3bt3etW3bVg0aNEiNHj1a4RegWLZt26YWLlyok0ZM9v/QX34uW6YTZEyud5Q/uYCHOjO/vXBBZ/BvV3AgHtBvzPUb6uxY0y83DfvewBW/cQWH9Ltcw+NSe8N4Zm48c0Vn9H9z+zOu9TcZzxjPpN9RVulaPHMND9tNc9sbxmfz4jP931x/qap/lpg44s/Q4WcyQAbIABkgA2TAXQaQiXv//ferrl276sSKvXv36pdBWMdDlyzr1q1TK1as0MfINlPLIUOGqLFjx+rq2T7SCEC4gIc6M9Vbmurlgs5c8RexCv1GmDCvdMVfwKxrOnMFjys4xHtdwwNcrsQBV3DAJtQZWDBvoV3Ms4m/Ri7Zh/HMb1nzPrtgH5f8BQpxDQ8wUWdgwbzFBbsIq674jSs4xC4oqTM/G7V/TkwcifrFK7fb/8vysjJueV2zM9RoH9qH8ZvxOy4OIPP7tttu0yOPBLsV+/fvV2+99ZbavHmzVSNvYZ7z8847Ty2zeKQRv9+6gIc6Mz8OuaAz+I0rOBC36Tfm+g11Zm7/2hW/cQWH9Gdcw+NSe8N4Zm48c0Vn9H9z+zOu9TcZzxjPpN9RVulaPHMND9tNc9sbxmfz4jP931x/qaJ/lpg4EnxpxHUyQAbIABkgA2TAfQZ69eql+vTpo7p06aK+++47tWPHDrVp0yYrRhlx3zruIKTO3LElkdSPAfpN/bhuzXdyTWeu4HEFh/iWa3gEF0uzGKDOzLKH1IZ2ESbMLGkf2sVMBsyslWv+4hoeM1VTe61ol9o5q+cZrtjHFRz1tH0971Uv+yQmjpSVkcnrmp3BRPvQPnEjElAf1Af1YV5mMP2Sfkm/pF8yDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDmSJA4mJI/XMluG9yAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABurHQGLiCDKSZGnMTPE2YKusc38DA8JHG/JDfdA/GB/aNGS0Mj4yPoIBtg+nM7w9Lth/aNJDg3c0rbP/QH3QP5r8gf7RwADbD7Yf+hdCbD/Zf/A0IPGA8ZHxEQyIHth/ZP+R/ccmf2B8ZHxkfGzyB7YPbB/YPjT5A9sHtg9sH5r8ge0D24fY9sF7r5mYOCJBlSUZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsiAWwwkJo5wDiTOgdT4S5bTIyhwnXOjMS4wLjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDrgRBxITR9zKkyEaMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQAWEgMXGEGUJuZAjRjrQjMz6Z8ck4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4EIwDiYkjkmHCkgyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZMAtBhITR4KZJlxnBhoz0JiBxjjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOOBGHEhMHHErT4ZoyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABoSBxMQRZAjJ0pgx5m3AVlnn/gYGhI825If6oH8wPrRpyDBlfGR8BANsH05nXHtcsP/QpIcG72haZ/+B+qB/NPkD/aOBAbYfbD/0L3bYfrL/4GlA4gHjI+MjGBA9sP/I/iP7j03+wPjI+Mj42OQPbB/YPrB9aPIHtg9sH9g+NPkD2we2D7Htg/deMzFxRIIqSzJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAG3GEhMHOGcRG7MSUQ70o6Nv0g6PRIG1znnHOMC4wLjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAONAYuKIW3kyREMGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATIgDCQmjjDDjBlmzDBjhhnjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOMA4wDjAOOAm3EgMXFEMkxYkgEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADLjFQGLiCDOG3MwYol1pV2aEMiOUcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcSAxccStPBmiIQNkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGRAGEhNHkGEmS2OmkbcBW2Wd+xsYED7akB/qg/7B+NCmITOR8ZHxEQywfTidqetxwf5Dkx4avKNpnf0H6oP+0eQP9I8GBth+sP3Qv/hi+8n+g6cBiQeMj4yPYED0wP4j+4/sPzb5A+Mj4yPjY5M/sH1g+8D2ockf2D6wfWD70OQPbB/YPsS2D957zcTEEQmqLMkAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbcYiAxcYRzWnFOq8ZfspweQYHrnOOKcYFxgXGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxwI04kJg44laeDNGQATJABsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMCAOJiSPMEHIjQ4h2pB2Z8cmMT8YBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcaBYBxITByRDBOWZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyQAbIABkgA24xkJg4Esw04Toz0JiBxgw0xgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGAcYBxgHGATfiQGLiiFt5MkRDBsgAGSADZIAMkAEyQAbIABkgA2SADJABMkAGyAAZIANkgAyQATJABsgAGSADZIAMkAEyIAz4E0f+P+62JaMi/XR/AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "id": "81d83044-be95-494a-819a-b58348dd9307",
   "metadata": {},
   "source": [
    "![image.png](attachment:7c3b6d49-4e52-4fbc-8129-b51f46fdf098.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495cb8a0-02bc-4c2c-8a7c-970ccba7e884",
   "metadata": {},
   "source": [
    "These are the counts of the sampled bit-strings, out of the 500 samples we drew from the circuit. You can see that few of the bit strings were sampled with a much higher probability. Let's Extract `x` and `y` values from the sampling results, to see whether we've got inputs for \"c\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d255147-bc73-4c75-b5e8-3de6821a3bdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:33:07.558317Z",
     "iopub.status.busy": "2024-05-07T15:33:07.557764Z",
     "iopub.status.idle": "2024-05-07T15:33:07.575861Z",
     "shell.execute_reply": "2024-05-07T15:33:07.575244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'c' satisfying sample: 6.0\n",
      "'c' satisfying sample: 6.0\n",
      "'c' satisfying sample: 2.0\n",
      "'c' satisfying sample: 6.0\n"
     ]
    }
   ],
   "source": [
    "samples = []\n",
    "for x, y, shot in [\n",
    "    [sample.state[\"x\"], sample.state[\"y\"], sample.shots]\n",
    "    for sample in results.parsed_counts\n",
    "]:\n",
    "    foo_out = foo(x, y)\n",
    "    samples = samples + [foo_out] * shot\n",
    "    if foo_out == \"c\":\n",
    "        print(f\"'c' satisfying sample: {x + y}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62c1be9f-cfac-487b-95ff-2d05d05b07cc",
   "metadata": {},
   "source": [
    "Which exactly corresponds to the 4 peaks in the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "01c33c08-4a90-48ae-9f6a-5ef692dad674",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T15:33:07.578393Z",
     "iopub.status.busy": "2024-05-07T15:33:07.578017Z",
     "iopub.status.idle": "2024-05-07T15:33:07.681801Z",
     "shell.execute_reply": "2024-05-07T15:33:07.680671Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_outputs(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd171efd-69db-4513-95e9-ed66fbc3c895",
   "metadata": {},
   "source": [
    "And \"c\" was indeed sampled with higher probability! If we did 36 grover repetitions, we would expect to get c with probability ~1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d84e3873-0882-4532-bee4-fd68d1c391fd",
   "metadata": {},
   "source": [
    "## Notes\n",
    "- While potentially \"black-box\" fuzzing can also benefit from quantum computers, it requires in general a high amount of quantum resources, in order to emulate the state of the classical program. On the otherhand, the \"white-box\" case is a lower hangning fruit, where a hybrid quantum-classical approach can be taken, with less resources required.\n",
    "- Here we showed quadratic improvement in comparison to a classical \"brute-force\" solver. However in reality there are much faster classical solvers. As a basic example, a solver can \"prune\" branches of search by backtracking in case a partial assignment is already not satisfiable. Such modifications are in general feasible also on a quantum computer. For example, see [[5](#Backtrack)] on Quantum Backtracking."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ebdad9-9a67-4e80-89e1-a459f282e851",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "<a name='Whitebox'>[1]</a>: [E. Bounimova, P. Godefroid and D. Molnar, \"Billions and billions of constraints: Whitebox fuzz testing in production,\" 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, USA, 2013, pp. 122-131, doi: 10.1109/ICSE.2013.6606558.](https://ieeexplore.ieee.org/document/6606558)\n",
    "\n",
    "<a name='Gro97'>[2]</a>: [Grover, Lov K. \"Quantum mechanics helps in searching for a needle in a haystack.\" Physical review letters 79.2 (1997): 325.](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.79.325)\n",
    "\n",
    "<a name='GroWiki'>[3]</a>: [Grover's algorithm (Wikipedia)](https://en.wikipedia.org/wiki/Grover%27s_algorithm)\n",
    "\n",
    "<a name='FPAA'>[4]</a>: [Yoder, Theodore J. et al. “Fixed-point quantum search with an optimal number of queries.” Physical review letters 113 21 (2014): 210501](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.113.210501)\n",
    "\n",
    "<a name='Backtrack'>[5]</a>: [Montanaro, Ashley. (2015). Quantum walk speedup of backtracking algorithms. Theory of Computing. 14. 10.4086/toc.2018.v014a015](https://theoryofcomputing.org/articles/v014a015/)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
